A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon
Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.
Which solution meets these requirements?

A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the data catalog in Aurora. Schedule the Lambda functions periodically.

B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes. Schedule the crawlers periodically to update the metadata catalog.

C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalog. Schedule the Lambda functions periodically.

D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.

A company's data analyst needs to ensure that queries run in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes.
Queries that exceed the prescribed threshold must be canceled immediately.
What should the data analyst do to achieve this?

A. Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.

B. For each workgroup, set the control limit for each query to the prescribed threshold.

C. Enforce the prescribed threshold on all Amazon S3 bucket policies

D. For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?

A. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

B. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.

C. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

D. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step
Functions for process orchestration, and Amazon CloudWatch for job scheduling.
More testing facilities were recently added, and the time to process files is increasing.
What will MOST efficiently decrease the data processing time?

A. Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.

B. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.

C. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.

D. Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.

A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.
Which solution will meet these requirements?

A. Use Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.

B. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.

C. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.

D. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?

A. Use Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.

B. Use Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a Kinesis Agent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.

C. Use Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.

D. Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

An online retail company is migrating its reporting system to AWS. The company's legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

A. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are updated. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

B. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an Amazon EMR cluster with consistent view enabled. Run emrfs sync before each analytics step to ensure data changes are updated. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

C. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw dataset. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

D. Use an S3 Select query to ensure that the data is properly updated. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

A company recently created a test AWS account to use for a development environment. The company also created a production AWS account in another AWS
Region. As part of its security testing, the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account.
Which solution will allow the company to accomplish this goal?

A. Create a subscription filter in the production account’s CloudWatch Logs to target the Kinesis data stream in the test account as its destination. In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account.

B. In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.

C. In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.

D. Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account. Create a subscription filter in the production account’s CloudWatch Logs to target the Kinesis data stream in the test account as its destination.

A marketing company has data in Salesforce, MySQL, and Amazon S3. The company wants to use data from these three locations and create mobile dashboards for its users. The company is unsure how it should create the dashboards and needs a solution with the least possible customization and coding.
Which solution meets these requirements?

A. Use Amazon Athena federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.

B. Use AWS Lake Formation to migrate the data sources into Amazon S3. Use Amazon QuickSight to generate the mobile dashboards.

C. Use Amazon Redshift federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.

D. Use Amazon QuickSight to connect to the data sources and generate the mobile dashboards.

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in
Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well- functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.

B. Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.

C. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.

D. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

A. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.

B. Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.

C. Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

D. Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?

A. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.

B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.

C. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

A. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.

B. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.

C. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key. Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.

D. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors.
Which solution enables the company to meet these requirements?

A. Set the RecordMaxBufferedTime property of the KPL to “גˆ’1” to disable the buffering on the sensor side. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.

B. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.

C. Set the RecordMaxBufferedTime property of the KPL to “0” to disable the buffering on the sensor side. Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten the JSON file before sending it to an Amazon S3 bucket. Load the S3 data into an Amazon Redshift cluster.

D. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.

A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately. The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer.
Which solution would achieve this goal?

A. Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature while consuming the data.

B. Have the app call the PutRecordBatch API to send data to Amazon Kinesis Data Firehose. Submit a support case to enable dedicated throughput on the account.

C. Have the app use Amazon Kinesis Producer Library (KPL) to send data to Kinesis Data Firehose. Use the enhanced fan-out feature while consuming the data.

D. Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Host the stream-processing application on Amazon EC2 with Auto Scaling.

A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate
COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?

A. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

B. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.

C. Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

D. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner.
Which solution meets these requirements?

A. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.

B. Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

C. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

D. Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data.

An education provider's learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider's LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance.
System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?

A. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.

B. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.

C. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.

D. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.

A company has multiple data workflows to ingest data from its operational databases into its data lake on Amazon S3. The workflows use AWS Glue and Amazon
EMR for data processing and ETL. The company wants to enhance its architecture to provide automated orchestration and minimize manual intervention.
Which solution should the company use to manage the data workflows to meet these requirements?

A. AWS Glue workflows

B. AWS Step Functions

C. AWS Lambda

D. AWS Batch

A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements.
Which solution meets the company's requirements?

A. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.

B. Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.

C. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.

D. Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.

A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation.
Which combination of steps is required to achieve compliance? (Choose two.)

A. Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.

B. Modify the cluster with an HSM encryption option and automatic key rotation.

C. Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.

D. Enable HSM with key rotation through the AWS CLI.

E. Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.

A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company's data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days.
The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration.
Which solution meets these requirements?

A. Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.

B. Schedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.

C. Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage.

D. Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

An online retailer needs to deploy a product sales reporting solution. The source data is exported from an external online transaction processing (OLTP) system for reporting. Roll-up data is calculated each day for the previous day's activities. The reporting system has the following requirements:
✑ Have the daily roll-up data readily available for 1 year.
✑ After 1 year, archive the daily roll-up data for occasional but immediate access.
✑ The source data exports stored in the reporting system must be retained for 5 years. Query access will be needed only for re-evaluation, which may occur within the first 90 days.
Which combination of actions will meet these requirements while keeping storage costs to a minimum? (Choose two.)

A. Store the source data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.

B. Store the source data initially in the Amazon S3 Glacier storage class. Apply a lifecycle configuration that changes the storage class from Amazon S3 Glacier to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.

C. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 1 year after data creation.

D. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) 1 year after data creation.

E. Store the daily roll-up data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier 1 year after data creation.

A company has 10-15 ׀¢׀’ of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine. The company wants to transform the data to optimize query runtime and storage costs.
Which option for data format and compression meets these requirements?

A. CSV compressed with zip

B. JSON compressed with bzip2

C. Apache Parquet compressed with Snappy

D. Apache Avro compressed with LZO

An online retailer is rebuilding its inventory management system and inventory reordering system to automatically reorder products by using Amazon Kinesis Data
Streams. The inventory management system uses the Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the
Kinesis Client Library (KCL) to consume data from the stream. The stream has been configured to scale as needed. Just before production deployment, the retailer discovers that the inventory reordering system is receiving duplicated data.
Which factors could be causing the duplicated data? (Choose two.)

A. The producer has a network-related timeout.

B. The stream’s value for the IteratorAgeMilliseconds metric is too high.

C. There was a change in the number of shards, record processors, or both.

D. The AggregationEnabled configuration property was set to true.

E. The max_records configuration property was set to a number that is too high.

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?

A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.

B. Enable job bookmarks on the AWS Glue jobs.

C. Create custom logic on the ETL jobs to track the processed S3 objects.

D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3.
The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data. The amount of data that is ingested into
Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)

A. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.

B. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.

C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.

D. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.

E. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.

A company uses Amazon Redshift to store its data. The reporting team runs ad-hoc queries to generate reports from the Amazon Redshift database. The reporting team recently started to experience inconsistencies in report generation. Ad-hoc queries used to generate reports that would typically take minutes to run can take hours to run. A data analytics specialist debugging the issue finds that ad-hoc queries are stuck in the queue behind long-running queries.
How should the data analytics specialist resolve the issue?

A. Create partitions in the tables queried in ad-hoc queries.

B. Configure automatic workload management (WLM) from the Amazon Redshift console.

C. Create Amazon Simple Queue Service (Amazon SQS) queues with different priorities. Assign queries to a queue based on priority.

D. Run the VACUUM command for all tables in the database.

A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third-party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.
Which options can fulfill these requirements? (Choose two.)

A. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.

B. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.

C. Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.

D. Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with DynamoDB Streams to install the software.

E. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance. Create an AMI and use that AMI to create the EMR cluster.

A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals.
The company wants to run an additional analysis on a year's worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week.
What is the MOST cost-effective solution?

A. Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1 year of data. Then use Amazon Redshift for the additional analysis.

B. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then use Amazon Redshift Spectrum for the additional analysis.

C. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then provision a persistent Amazon EMR cluster and use Apache Presto for the additional analysis.

D. Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each individual node in the Amazon Redshift cluster. Then use Amazon Redshift for the additional analysis.

A company wants to provide its data analysts with uninterrupted access to the data in its Amazon Redshift cluster. All data is streamed to an Amazon S3 bucket with Amazon Kinesis Data Firehose. An AWS Glue job that is scheduled to run every 5 minutes issues a COPY command to move the data into Amazon Redshift.
The amount of data delivered is uneven throughout the day, and cluster utilization is high during certain periods. The COPY command usually completes within a couple of seconds. However, when load spike occurs, locks can exist and data can be missed. Currently, the AWS Glue job is configured to run without retries, with timeout at 5 minutes and concurrency at 1.
How should a data analytics specialist configure the AWS Glue job to optimize fault tolerance and improve data availability in the Amazon Redshift cluster?

A. Increase the number of retries. Decrease the timeout value. Increase the job concurrency.

B. Keep the number of retries at 0. Decrease the timeout value. Increase the job concurrency.

C. Keep the number of retries at 0. Decrease the timeout value. Keep the job concurrency at 1.

D. Keep the number of retries at 0. Increase the timeout value. Keep the job concurrency at 1.

A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket. The reports are delivered to the S3 bucket in the following format:
//yyyymmdd-yyyymmdd/.parquet
An AWS Glue crawler crawls the S3 bucket and populates an AWS Glue Data Catalog with a table. Business analysts use Amazon Athena to query the table and create monthly summary reports for the AWS accounts. The business analysts are experiencing slow queries because of the accumulation of reports from the last
5 years. The business analysts want the operations team to make changes to improve query performance.
Which action should the operations team take to meet these requirements?

A. Change the file format to .csv.zip

B. Partition the data by date and account ID

C. Partition the data by month and account ID

D. Partition the data by account ID, year, and month

An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an
Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: `Command Failed with Exit Code 1.`
Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches
90`"95% soon after. The average memory usage across all executors continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.
 
What should the data engineer do to solve the failure in the MOST cost-effective way?

A. Change the worker type from Standard to G.2X.

B. Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.

C. Increase the fetch size setting by using AWS Glue dynamics frame.

D. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
How should the data analyst resolve the issue?

A. Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.

B. Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.

C. Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.

D. Edit the permissions for the new S3 bucket from within the S3 console.

A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

A. In Apache ORC partitioned by date and sorted by source IP

B. In compressed .csv partitioned by date and sorted by source IP

C. In Apache Parquet partitioned by source IP and sorted by date

D. In compressed nested JSON partitioned by source IP and sorted by date

A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?

A. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.

B. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.

C. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.

D. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

A company is providing analytics services to its sales and marketing departments. The departments can access the data only through their business intelligence
(BI) tools, which run queries on Amazon Redshift using an Amazon Redshift internal user to connect. Each department is assigned a user in the Amazon Redshift database with the permissions needed for that department. The marketing data analysts must be granted direct access to the advertising table, which is stored in
Apache Parquet format in the marketing S3 bucket of the company data lake. The company data lake is managed by AWS Lake Formation. Finally, access must be limited to the three promotion columns in the table.
Which combination of steps will meet these requirements? (Choose three.)

A. Grant permissions in Amazon Redshift to allow the marketing Amazon Redshift user to access the three promotion columns of the advertising external table.

B. Create an Amazon Redshift Spectrum IAM role with permissions for Lake Formation. Attach it to the Amazon Redshift cluster.

C. Create an Amazon Redshift Spectrum IAM role with permissions for the marketing S3 bucket. Attach it to the Amazon Redshift cluster.

D. Create an external schema in Amazon Redshift by using the Amazon Redshift Spectrum IAM role. Grant usage to the marketing Amazon Redshift user.

E. Grant permissions in Lake Formation to allow the Amazon Redshift Spectrum role to access the three promotion columns of the advertising table.

F. Grant permissions in Lake Formation to allow the marketing IAM group to access the three promotion columns of the advertising table.

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync.
Which solution will simplify permissions management with minimal development effort?

A. Set AWS Identity and Access Management (IAM) permissions for AWS Glue

B. Use AWS Lake Formation permissions

C. Manage AWS Glue and S3 permissions by using bucket policies

D. Use Amazon Cognito user pools

A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to
Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near- real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?

A. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.

B. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.

C. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the output to DynamoDB by using the default output from Kinesis Data Firehose.

D. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.

A company needs to store objects containing log data in JSON format. The objects are generated by eight applications running in AWS. Six of the applications generate a total of 500 KiB of data per second, and two of the applications can generate up to 2 MiB of data per second.
A data engineer wants to implement a scalable solution to capture and store usage data in an Amazon S3 bucket. The usage data objects need to be reformatted, converted to .csv format, and then compressed before they are stored in Amazon S3. The company requires the solution to include the least custom code possible and has authorized the data engineer to request a service quota increase if needed.
Which solution meets these requirements?

A. Configure an Amazon Kinesis Data Firehose delivery stream for each application. Write AWS Lambda functions to read log data objects from the stream for each application. Have the function perform reformatting and .csv conversion. Enable compression on all the delivery streams.

B. Configure an Amazon Kinesis data stream with one shard per application. Write an AWS Lambda function to read usage data objects from the shards. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.

C. Configure an Amazon Kinesis data stream for each application. Write an AWS Lambda function to read usage data objects from the stream for each application. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.

D. Store usage data objects in an Amazon DynamoDB table. Configure a DynamoDB stream to copy the objects to an S3 bucket. Configure an AWS Lambda function to be triggered when objects are written to the S3 bucket. Have the function convert the objects into .csv format.

A company is Running Apache Spark on an Amazon EMR cluster. The Spark job writes to an Amazon S3 bucket. The job fails and returns an HTTP 503 `Slow
Down` AmazonS3Exception error.
Which actions will resolve this error? (Choose two.)

A. Add additional prefixes to the S3 bucket

B. Reduce the number of prefixes in the S3 bucket

C. Increase the EMR File System (EMRFS) retry limit

D. Disable dynamic partition pruning in the Spark configuration for the cluster

E. Add more partitions in the Spark configuration for the cluster

A company using Amazon QuickSight Enterprise edition has thousands of dashboards, analyses, and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.
Which solution should the company implement to simplify permissions management?

A. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign individual users permissions to these folders.

B. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign group permissions by using these folders.

C. Use AWS IAM resource-based policies to assign group permissions to QuickSight items.

D. Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions.

A company is sending historical datasets to Amazon S3 for storage. A data engineer at the company wants to make these datasets available for analysis using
Amazon Athena. The engineer also wants to encrypt the Athena query results in an S3 results location by using AWS solutions for encryption. The requirements for encrypting the query results are as follows:
✑ Use custom keys for encryption of the primary dataset query results.
✑ Use generic encryption for all other query results.
✑ Provide an audit trail for the primary dataset queries that shows when the keys were used and by whom.
Which solution meets these requirements?

A. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primary dataset. Use SSE-S3 for the other datasets.

B. Use server-side encryption with customer-provided encryption keys (SSE-C) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.

C. Use server-side encryption with AWS KMS managed customer master keys (SSE-KMS CMKs) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.

D. Use client-side encryption with AWS Key Management Service (AWS KMS) customer managed keys for the primary dataset. Use S3 client-side encryption with client-side keys for the other datasets.

A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. The company has set a deadline for a consultant to build a data lake.
How should the consultant create the MOST cost-effective solution that meets these requirements?

A. Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.

B. To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.

C. Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create role-based access control for the existing data assets in Amazon S3.

D. Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.

An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)

A. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

B. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon OpenSearch Service (Amazon Elasticsearch Service) from Amazon S3.

C. Use Amazon OpenSearch Service (Amazon Elasticsearch Service) deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.

D. Use OpenSearch Dashboards (Kibana) to aggregate, filter, and visualize the data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service). Refresh content performance dashboards in near-real time.

E. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:
HIVE_METASTORE_ERROR -
The error message states that the response payload size exceeds the maximum allowed size. The queried table is already partitioned, and the data is stored in an
Amazon S3 bucket in the Apache Hive partition format.
Which solution will resolve this error?

A. Modify the Lambda function to upload the query response payload as an object into the S3 bucket. Include an S3 object presigned URL as the payload in the Lambda function response.

B. Run the MSCK REPAIR TABLE command on the queried table.

C. Create a separate folder in the S3 bucket. Move the data files that need to be queried into that folder. Create an AWS Glue crawler that points to the folder instead of the S3 bucket.

D. Check the schema of the queried table for any characters that Athena does not support. Replace any unsupported characters with characters that Athena supports.

A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both
Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?

A. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.

B. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.

C. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.

D. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2. Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.
A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.
How should the data analyst meet this requirement while minimizing costs?

A. Store the contents of the mapping file in an Amazon DynamoDB table. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Change the SQL query in the application to include the new field in the SELECT statement.

B. Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the .csv file in the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the file’s S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.

C. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.

D. Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exists. Forward the record from the Lambda function to the original application destination.

A large company receives files from external parties in Amazon EC2 throughout the day. At the end of the day, the files are combined into a single file, compressed into a gzip file, and uploaded to Amazon S3. The total size of all the files is close to 100 GB daily. Once the files are uploaded to Amazon S3, an
AWS Batch program executes a COPY command to load the files into an Amazon Redshift cluster.
Which program modification will accelerate the COPY process?

A. Upload the individual files to Amazon S3 and run the COPY command as soon as the files become available.

B. Split the number of files so they are equal to a multiple of the number of slices in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.

C. Split the number of files so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluster. Gzip and upload the files to Amazon S3. Run the COPY command on the files.

D. Apply sharding by breaking up the files so the distkey columns with the same values go to the same file. Gzip and upload the sharded files to Amazon S3. Run the COPY command on the files.

A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.
Which solution meets these requirements?

A. Resize the cluster using elastic resize with dense compute nodes.

B. Resize the cluster using classic resize with dense compute nodes.

C. Resize the cluster using elastic resize with dense storage nodes.

D. Resize the cluster using classic resize with dense storage nodes.