Suggested Answer:

Log type: PipelineRuns –
A pipeline run in Azure Data Factory defines an instance of a pipeline execution.
Storage location: An Azure Storage account
Data Factory stores pipeline-run data for only 45 days. Use Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis. You can also keep them in a storage account so that you have factory information for your chosen duration.
Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

Suggested Answer:

Box 1: transparent data encryption
TDE with customer-managed keys in Azure Key Vault allows to encrypt the Database Encryption Key (DEK) with a customer-managed asymmetric key called
TDE Protector. This is also generally referred to as Bring Your Own Key (BYOK) support for Transparent Data Encryption.
Note: Transparent data encryption encrypts the storage of an entire database by using a symmetric key called the database encryption key. This database encryption key is protected by the transparent data encryption protector.
Transparent data encryption (TDE) helps protect Azure SQL Database, Azure SQL Managed Instance, and Azure Data Warehouse against the threat of malicious offline activity by encrypting data at rest. It performs real-time encryption and decryption of the database, associated backups, and transaction log files at rest without requiring changes to the application.
Box 2: Storage account keys –
You can rely on Microsoft-managed keys for the encryption of your storage account, or you can manage encryption with your own keys, together with Azure Key
Vault.
References:
https://docs.microsoft.com/en-us/azure/sql-database/transparent-data-encryption-azure-sql
https://docs.microsoft.com/en-us/azure/storage/common/storage-service-encryption

Suggested Answer:

Box 1: NameNode –
An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients.
Box 2: DataNode –
The DataNodes are responsible for serving read and write requests from the file system’s clients.
Box 3: DataNode –
The DataNodes perform block creation, deletion, and replication upon instruction from the NameNode.
Note: HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode.
References:
https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html#NameNode+and+DataNodes

Suggested Answer:

Box 1: fail until the node comes back online
We see: High Availability Enabled: False
Note: Higher availability of the self-hosted integration runtime so that it’s no longer the single point of failure in your big data solution or cloud data integration with
Data Factory.
Box 2: lowered –
We see:
Concurrent Jobs (Running/Limit): 2/14
CPU Utilization: 6%
Note: When the processor and available RAM aren’t well utilized, but the execution of concurrent jobs reaches a node’s limits, scale up by increasing the number of concurrent jobs that a node can run
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

Suggested Answer: B

Instead modify the files to ensure that each row is less than 1 MB.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

Suggested Answer: B

Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started-portal

Suggested Answer:

Step 1: From the Azure Portal, set the Active Directory admin for SQL1.
Provision an Azure Active Directory administrator for your Azure SQL Database server.
You can provision an Azure Active Directory administrator for your Azure SQL server in the Azure portal and by using PowerShell.
Step 2: On DB1, create a contained user for the Analysts group by using Transact-SQL
Create contained database users in your database mapped to Azure AD identities.
To create an Azure AD-based contained database user (other than the server administrator that owns the database), connect to the database with an Azure AD identity, as a user with at least the ALTER ANY USER permission. Then use the following Transact-SQL syntax:
CREATE USER FROM EXTERNAL PROVIDER;
Step 3: From Microsoft SQL Server Management Studio (SSMS), sign in to SQL1 by using the account set as the Active Directory admin.
Connect to the user database or data warehouse by using SSMS or SSDT
To confirm the Azure AD administrator is properly set up, connect to the master database using the Azure AD administrator account. To provision an Azure AD- based contained database user (other than the server administrator that owns the database), connect to the database with an Azure AD identity that has access to the database.
Step 4: On DB1, grant the VIEW and SELECT DEFINTION..
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-aad-authentication-configure

Suggested Answer: See the explanation below.

Set up auditing for your database
The following section describes the configuration of auditing using the Azure portal.
1. Go to the Azure portal.
2. Navigate to Auditing under the Security heading in your SQL database db1/server pane

3. If you prefer to enable auditing on the database level, switch Auditing to ON.

Reference: alt=”Reference Image” />
3. If you prefer to enable auditing on the database level, switch Auditing to ON.
<img src=”https://www.examtopics.com/assets/media/exam-media/03872/0038200001.jpg” alt=”Reference Image” />
Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-auditing

Suggested Answer: B

Instead configure Azure Data Lake Storage diagnostics to store logs and metrics in a storage account.
Note:
You can enable diagnostic logging for your Azure Data Lake Storage Gen1 accounts, blobs, files, queues and tables.
Diagnostic logs aren’t available for Data Lake Storage Gen2 accounts [as of August 2019].
Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-diagnostic-logs
https://github.com/MicrosoftDocs/azure-docs/issues/34286

Suggested Answer: B

Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/alerts-insights-configure-portal

Suggested Answer: A

From the Azure Storage account that contains log data, open the Azure Storage account blade associated with Data Lake Storage Gen1 for logging, and then click Blobs. The Blob service blade lists two containers.

Note:
You can enable diagnostic logging for your Azure Data Lake Storage Gen1 accounts, blobs, files, queues and tables.
Diagnostic logs aren’t available for Data Lake Storage Gen2 accounts [as of August 2019].
Reference: alt=”Reference Image” />
Note:
You can enable diagnostic logging for your Azure Data Lake Storage Gen1 accounts, blobs, files, queues and tables.
Diagnostic logs aren’t available for Data Lake Storage Gen2 accounts [as of August 2019].
Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-diagnostic-logs
https://github.com/MicrosoftDocs/azure-docs/issues/34286

Suggested Answer: C

Customer Scenario:
SQL Server 2016 or SQL DW connected to Azure blob storage. The CREATE EXTERNAL TABLE DDL points to a directory (and not a specific file) and the directory contains files with different schemas.
SSMS Error:
Select query on the external table gives the following error:
Msg 7320, Level 16, State 110, Line 14
Cannot execute the query “Remote Query” against OLE DB provider “SQLNCLI11” for linked server “(null)”. Query aborted– the maximum reject threshold (0 rows) was reached while reading from an external source: 1 rows rejected out of total 1 rows processed.
Possible Reason:
The reason this error happens is because each file has different schema. The PolyBase external table DDL when pointed to a directory recursively reads all the files in that directory. When a column or data type mismatch happens, this error could be seen in SSMS.
Possible Solution:
If the data for each table consists of one file, then use the filename in the LOCATION section prepended by the directory of the external files. If there are multiple files per table, put each set of files into different directories in Azure Blob Storage and then you can point LOCATION to the directory instead of a particular file.
The latter suggestion is the best practices recommended by SQLCAT even if you have one file per table.
Incorrect Answers:
A: Possible Reason: Kerberos is not enabled in Hadoop Cluster.
References:
https://techcommunity.microsoft.com/t5/DataCAT/PolyBase-Setup-Errors-and-Possible-Solutions/ba-p/305297

Suggested Answer: B

Instead apply an Azure Blob storage lifecycle policy.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts?tabs=azure-portal

Suggested Answer: See the explanation below.

1. In the Azure portal, browse to the database that you want to set up for geo-replication.
2. On the SQL database page, select geo-replication, and then select the region to create the secondary database.

3. Select or configure the server and for the secondary database.
Region: Central Canada –
Target server: REPL10277521 –

4. Click Create to add the secondary.
5. The secondary database is created and the seeding process begins.

6. When the seeding process is complete, the secondary database displays its status.

References: alt=”Reference Image” />
3. Select or configure the server and for the secondary database.
Region: Central Canada –
Target server: REPL10277521 –

4. Click Create to add the secondary.
5. The secondary database is created and the seeding process begins.

6. When the seeding process is complete, the secondary database displays its status.
<img src=”https://www.examtopics.com/assets/media/exam-media/03872/0005700001.png” alt=”Reference Image” />
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-active-geo-replication-portal

Suggested Answer: BD

B: Late Input Events: events that arrived later than the configured late arrival tolerance window.
Note: While comparing utilization over a period of time, use event rate metrics. InputEvents and OutputEvents metrics show how many events were read and processed.
D: In job diagram, there is a per partition backlog event metric for each input. If the backlog event metric keeps increasing, it’s also an indicator that the system resource is constrained (either because of output sink throttling, or high CPU).
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs

Suggested Answer:

Account type: StorageV2 –
You must create new storage accounts as type StorageV2 (general-purpose V2) to take advantage of Data Lake Storage Gen2 features.
Scenario: Polling data is stored in one of the two locations:
✑ An on-premises Microsoft SQL Server 2019 database named PollingData
✑ Azure Data Lake Gen 2
Data in Data Lake is queried by using PolyBase
Replication type: RA-GRS –
Scenario: All services and processes must be resilient to a regional Azure outage.
Geo-redundant storage (GRS) is designed to provide at least 99.99999999999999% (16 9’s) durability of objects over a given year by replicating your data to a secondary region that is hundreds of miles away from the primary region. If your storage account has GRS enabled, then your data is durable even in the case of a complete regional outage or a disaster in which the primary region isn’t recoverable.
If you opt for GRS, you have two related options to choose from:
✑ GRS replicates your data to another data center in a secondary region, but that data is available to be read only if Microsoft initiates a failover from the primary to secondary region.
✑ Read-access geo-redundant storage (RA-GRS) is based on GRS. RA-GRS replicates your data to another data center in a secondary region, and also provides you with the option to read from the secondary region. With RA-GRS, you can read from the secondary region regardless of whether Microsoft initiates a failover from the primary to secondary region.
References:
https://docs.microsoft.com/bs-cyrl-ba/azure/storage/blobs/data-lake-storage-quickstart-create-account
https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy-grs

Suggested Answer: D

Linked services are much like connection strings, which define the connection information needed for Data Factory to connect to external resources.
Incorrect Answers:
A, C: Data Factory requires two properties to be set on the Key Vault, Soft Delete and Do Not Purge
B: A self-hosted integration runtime copies data between an on-premises store and cloud storage.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/enable-customer-managed-key
https://docs.microsoft.com/en-us/azure/data-factory/concepts-linked-services
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

Suggested Answer: B

Azure Monitor logs integration. Azure Monitor logs enables data generated by multiple resources such as HDInsight clusters, to be collected and aggregated in one place to achieve a unified monitoring experience.
As a prerequisite, you will need a Log Analytics Workspace to store the collected data. If you have not already created one, you can follow the instructions for creating a Log Analytics Workspace.
You can then easily configure an HDInsight cluster to send many workload-specific metrics to Log Analytics.
References:
https://azure.microsoft.com/sv-se/blog/monitoring-on-azure-hdinsight-part-2-cluster-health-and-availability/

Suggested Answer: C

Append Mode: Only new rows appended in the result table since the last trigger are written to external storage. This is applicable only for the queries where existing rows in the Result Table are not expected to change.
Incorrect Answers:
A: Complete Mode: The entire updated result table is written to external storage. It is up to the storage connector to decide how to handle the writing of the entire table.
B: Update Mode: Only the rows that were updated in the result table since the last trigger are written to external storage. This is different from Complete Mode in that Update Mode outputs only the rows that have changed since the last trigger. If the query doesn’t contain aggregations, it is equivalent to Append mode.
Reference:
https://docs.databricks.com/getting-started/spark/streaming.html

Suggested Answer: AB

Success E2E Latency: The average end-to-end latency of successful requests made to a storage service or the specified API operation. This value includes the required processing time within Azure Storage to read the request, send the response, and receive acknowledgment of the response.
Success Server Latency: The average time used to process a successful request by Azure Storage. This value does not include the network latency specified in
SuccessE2ELatency.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-scalable-app-verify-metrics

Suggested Answer: AEF

A: Premium/business critical service tier model that is based on a cluster of database engine processes. This architectural model relies on a fact that there is always a quorum of available database engine nodes and has minimal performance impact on your workload even during maintenance activities.
E: In the premium model, Azure SQL database integrates compute and storage on the single node. High availability in this architectural model is achieved by replication of compute (SQL Server Database Engine process) and storage (locally attached SSD) deployed in 4-node cluster, using technology similar to SQL
Server Always On Availability Groups.

F: Zone redundant configuration –
By default, the quorum-set replicas for the local storage configurations are created in the same datacenter. With the introduction of Azure Availability Zones, you have the ability to place the different replicas in the quorum-sets to different availability zones in the same region. To eliminate a single point of failure, the control ring is also duplicated across multiple zones as three gateway rings (GW).
References: alt=”Reference Image” />
F: Zone redundant configuration –
By default, the quorum-set replicas for the local storage configurations are created in the same datacenter. With the introduction of Azure Availability Zones, you have the ability to place the different replicas in the quorum-sets to different availability zones in the same region. To eliminate a single point of failure, the control ring is also duplicated across multiple zones as three gateway rings (GW).
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-high-availability

Suggested Answer:

The way you control access to resources using RBAC is to create role assignments. This is a key concept to understand ג€” it’s how permissions are enforced. A role assignment consists of three elements: security principal, role definition, and scope.
Scenario:
No credentials or secrets should be used during deployments
Phone-based poll data must only be uploaded by authorized users from authorized devices
Contractors must not have access to any polling data other than their own
Access to polling data must set on a per-active directory user basis
References:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview

Suggested Answer:

Step 1: Read the file into a data frame.
You can load the json files as a data frame in Azure Databricks.
Step 2: Perform transformations on the data frame.
Step 3:Specify a temporary folder to stage the data
Specify a temporary folder to use while moving data between Azure Databricks and Azure Synapse.
Step 4: Write the results to a table in Azure Synapse.
You upload the transformed data frame into Azure Synapse. You use the Azure Synapse connector for Azure Databricks to directly upload a dataframe as a table in a Azure Synapse.
Step 5: Drop the data frame –
Clean up resources. You can terminate the cluster. From the Azure Databricks workspace, select Clusters on the left. For the cluster to terminate, under Actions, point to the ellipsis (…) and select the Terminate icon.
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse

Suggested Answer: B

Instead convert the files to compressed delimited text files.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

Suggested Answer:

Step 1: Enable advanced threat protection
Set up threat detection for your database in the Azure portal
1. Launch the Azure portal at https://portal.azure.com.
2. Navigate to the configuration page of the Azure SQL Database server you want to protect. In the security settings, select Advanced Data Security.
3. On the Advanced Data Security configuration page:
Enable advanced data security on the server.
In Threat Detection Settings, in the Send alerts to text box, provide the list of emails to receive security alerts upon detection of anomalous database activities.

Step 2: Configure the service to send email alerts to
security@contoso.team
Step 3:..of type data exfiltration
The benefits of Advanced Threat Protection for Azure Storage include:
Detection of anomalous access and data exfiltration activities.
Security alerts are triggered when anomalies in activity occur: access from an unusual location, anonymous access, access by an unusual application, data exfiltration, unexpected delete operations, access permission change, and so on.
Admins can view these alerts via Azure Security Center and can also choose to be notified of each of them via email.
References:https://portal.azure.com.

2. Navigate to the configuration page of the Azure SQL Database server you want to protect. In the security settings, select Advanced Data Security.
3. On the Advanced Data Security configuration page:
Enable advanced data security on the server.
In Threat Detection Settings, in the Send alerts to text box, provide the list of emails to receive security alerts upon detection of anomalous database activities.
<img src=”https://www.examtopics.com/assets/media/exam-media/03872/0033200001.jpg” alt=”Reference Image” />
Step 2: Configure the service to send email alerts to
security@contoso.team
Step 3:..of type data exfiltration
The benefits of Advanced Threat Protection for Azure Storage include:
Detection of anomalous access and data exfiltration activities.
Security alerts are triggered when anomalies in activity occur: access from an unusual location, anonymous access, access by an unusual application, data exfiltration, unexpected delete operations, access permission change, and so on.
Admins can view these alerts via Azure Security Center and can also choose to be notified of each of them via email.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-threat-detection
https://www.helpnetsecurity.com/2019/04/04/microsoft-azure-security/

Suggested Answer: ACD

The Microsoft documentation highlights the steps required to load data from Azure Data Lake Gen2 to an Azure SQL Data warehouse.
One of the steps is to create a database scoped credential:

Another step is to create the external data source using ‘abfs’ as the file location:
Create the external data source –
Use this CREATE EXTERNAL DATA SOURCE command to store the location of the data.

And you can use the FIRST_ROW parameter to skip the first row of the file.
FIRST_ROW = First_row_int –
Specifies the row number that is read first in all files during a PolyBase load. This parameter can take values 1-15. If the value is set to two, the first row in every file (header row) is skipped when the data is loaded. Rows are skipped based on the existence of row terminators (/r/n, /r, /n). When this option is used for export, rows are added to the data to make sure the file can be read with no data loss. If the value is set to >2, the first row exported is the Column names of the external table.
Reference: alt=”Reference Image” />
Another step is to create the external data source using ‘abfs’ as the file location:
Create the external data source –
Use this CREATE EXTERNAL DATA SOURCE command to store the location of the data.
<img src=”https://www.examtopics.com/assets/media/exam-media/03872/0060600001.jpg” alt=”Reference Image” />
And you can use the FIRST_ROW parameter to skip the first row of the file.
FIRST_ROW = First_row_int –
Specifies the row number that is read first in all files during a PolyBase load. This parameter can take values 1-15. If the value is set to two, the first row in every file (header row) is skipped when the data is loaded. Rows are skipped based on the existence of row terminators (/r/n, /r, /n). When this option is used for export, rows are added to the data to make sure the file can be read with no data loss. If the value is set to >2, the first row exported is the Column names of the external table.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-external-file-format-transact-sql?view=sql-server-ver15

Suggested Answer:

Step 1: Create an Azure key vault and enable purge protection
Using customer-managed keys with Azure Cosmos DB requires you to set two properties on the Azure Key Vault instance that you plan to use to host your encryption keys: Soft Delete and Purge Protection.
Step 2: Create a new Azure Cosmos DB account, set Data Encryption to Customer-managed Key (Enter key URI), and enter the key URI
Data stored in your Azure Cosmos account is automatically and seamlessly encrypted with keys managed by Microsoft (service-managed keys). Optionally, you can choose to add a second layer of encryption with keys you manage (customer-managed keys).
Step 3: Add an Azure Key Vault access policy to grant permissions to the Azure Cosmos DB principal
Add an access policy to your Azure Key Vault instance
Step 4: Generate a new key in the Azure key vault
Generate a key in Azure Key Vault
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/how-to-setup-cmk

Suggested Answer: B

Instead you can use the Delete Activity in Azure Data Factory to delete files or folders from on-premises storage stores or cloud storage stores or apply an Azure
Blob storage lifecycle policy.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/delete-activity
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts?tabs=azure-portal

Suggested Answer: A

One option is to have a lookup collection ג€ProductNameג€ for the mapping of ג€ProductNameג€ to ג€OrderIdג€.
References:
https://azure.microsoft.com/sv-se/blog/azure-cosmos-db-partitioning-design-patterns-part-1/

Suggested Answer: C

Reference:
https://docs.microsoft.com/en-us/rest/api/storageservices/sas-error-codes

Suggested Answer: BCE

The Microsoft documentation clearly gives the steps to create and configure the database. Please note the below snippet shows the new powershell commands, but you can also use the older Azure PowerShell commands.

Since this is clearly given in the documentation, all other options are incorrect.
Reference: alt=”Reference Image” />
Since this is clearly given in the documentation, all other options are incorrect.
Reference:
https://docs.microsoft.com/en-us/azure/sql-database/scripts/sql-database-create-and-configure-database-powershell

Suggested Answer:

Box 1: GRANT –
You can implement column-level security with the GRANT T-SQL statement.
Box 2: CREATE SECURITY POLICY –
Implement Row Level Security by using the CREATE SECURITY POLICY Transact-SQL statement
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/column-level-security

Suggested Answer: A

The following table describes the capabilities and network support for each of the integration runtime types:

Scenario: The solution must support migrating databases that support external and internal application to Azure SQL Database. The migrated databases will be supported by Azure Data Factory pipelines for the continued movement, migration and updating of data both in the cloud and from local core business systems and repositories.
References: alt=”Reference Image” />
Scenario: The solution must support migrating databases that support external and internal application to Azure SQL Database. The migrated databases will be supported by Azure Data Factory pipelines for the continued movement, migration and updating of data both in the cloud and from local core business systems and repositories.
References:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime

Suggested Answer: B

There is no need to convert the parquet files to CSV files.
You load the data using the CREATE TABLE AS SELECT statement.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store

Suggested Answer: B

Instead apply an Azure Blob storage lifecycle policy.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts?tabs=azure-portal

Suggested Answer:

Step 1: Create a master key on the database
Create a master key on the database. This is required to encrypt the credential secret.
Step 2: Create an external data source for Azure Blob storage
Create an external data source with CREATE EXTERNAL DATA SOURCE..
Step 3: Create an external file format to map parquet files.
Create an external file format with CREATE EXTERNAL FILE FORMAT.
FORMAT TYPE: Type of format in Hadoop (DELIMITEDTEXT, RCFILE, ORC, PARQUET).
Step 4: Create the external table FactSalesOrderDetails
To query the data in your Hadoop data source, you must define an external table to use in Transact-SQL queries.
Create an external table pointing to data stored in Azure storage with CREATE EXTERNAL TABLE.
Note: PolyBase is a technology that accesses and combines both non-relational and relational data, all from within SQL Server. It allows you to run queries on external data in Hadoop or Azure blob storage.
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/polybase/polybase-configure-azure-blob-storage

Suggested Answer: E

The best size for a pool depends on the aggregate resources needed for all databases in the pool. This involves determining the following:
✑ Maximum resources utilized by all databases in the pool (either maximum DTUs or maximum vCores depending on your choice of resourcing model).
✑ Maximum storage bytes utilized by all databases in the pool.
Note: Elastic pools enable the developer to purchase resources for a pool shared by multiple databases to accommodate unpredictable periods of usage by individual databases. You can configure resources for the pool based either on the DTU-based purchasing model or the vCore-based purchasing model.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-elastic-pool

Suggested Answer: A

You can use the Delete Activity in Azure Data Factory to delete files or folders from on-premises storage stores or cloud storage stores.
Azure Blob storage is supported.
Note: You can also apply an Azure Blob storage lifecycle policy.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/delete-activity
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-lifecycle-management-concepts?tabs=azure-portal

Suggested Answer: B

You need to create an external file format and external table using the external data source.
You then load the data using the CREATE TABLE AS SELECT statement.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store

Suggested Answer: C

Stream Analytics supports Azure Blob storage and Azure SQL Database as the storage layer for Reference Data.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data

Suggested Answer:

Step 1: Create an Azure Storage account that has a lifecycle policy
To automate common data management tasks, Microsoft created a solution based on Azure Data Factory. The service, Data Lifecycle Management, makes frequently accessed data available and archives or purges other data according to retention policies. Teams across the company use the service to reduce storage costs, improve app performance, and comply with data retention policies.
Step 2: Create a Log Analytics workspace that has Data Retention set to 120 days.
Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis to multiple different targets, such as a Storage Account: Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
Step 3: From Azure Portal, add a diagnostic setting.
Step 4: Send the data to a log Analytics workspace,
Event Hub: A pipeline that transfers events from services to Azure Data Explorer.
Keeping Azure Data Factory metrics and pipeline-run data.
Configure diagnostic settings and workspace.
Create or add diagnostic settings for your data factory.
1. In the portal, go to Monitor. Select Settings > Diagnostic settings.
2. Select the data factory for which you want to set a diagnostic setting.
3. If no settings exist on the selected data factory, you’re prompted to create a setting. Select Turn on diagnostics.
4. Give your setting a name, select Send to Log Analytics, and then select a workspace from Log Analytics Workspace.
5. Select Save.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

Suggested Answer:

Box 1: Source –
For Copy activity, it requires source and sink linked services to define the direction of data flow.
Copying between a cloud data source and a data source in private network: if either source or sink linked service points to a self-hosted IR, the copy activity is executed on that self-hosted Integration Runtime.
Box 2: Self-hosted integration runtime
A self-hosted integration runtime can run copy activities between a cloud data store and a data store in a private network, and it can dispatch transform activities against compute resources in an on-premises network or an Azure virtual network. The installation of a self-hosted integration runtime needs on an on-premises machine or a virtual machine (VM) inside a private network.
References:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

Suggested Answer:

Box 1: Azure Cosmos DB –
The DocumentClient.CreateDatabaseAsync(Database, RequestOptions) method creates a database resource as an asychronous operation in the Azure Cosmos
DB service.
Box 2: Master Key –
Azure Cosmos DB uses two types of keys to authenticate users and provide access to its data and resources: Master Key, Resource Tokens
Master keys provide access to the all the administrative resources for the database account. Master keys:
Provide access to accounts, databases, users, and permissions.

✑ Cannot be used to provide granular access to containers and documents.
✑ Are created during the creation of an account.
✑ Can be regenerated at any time.
Incorrect Answers:
Resource Token: Resource tokens provide access to the application resources within a database.
References: alt=”Reference Image” />
✑ Cannot be used to provide granular access to containers and documents.
✑ Are created during the creation of an account.
✑ Can be regenerated at any time.
Incorrect Answers:
Resource Token: Resource tokens provide access to the application resources within a database.
References:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.documents.client.documentclient.createdatabaseasync
https://docs.microsoft.com/en-us/azure/cosmos-db/secure-access-to-data

Suggested Answer: D

You can create high availability clusters of On-premises data gateway installations, to ensure your organization can access on-premises data resources used in
Power BI reports and dashboards. Such clusters allow gateway administrators to group gateways to avoid single points of failure in accessing on-premises data resources. The Power BI service always uses the primary gateway in the cluster, unless it’s not available. In that case, the service switches to the next gateway in the cluster, and so on.
References:
https://docs.microsoft.com/en-us/power-bi/service-gateway-high-availability-clusters

Suggested Answer:

Box 1: Hash –
Hash-distributed tables improve query performance on large fact tables. hey can have very large numbers of rows and still achieve high performance.
Incorrect:
Round-robin tables are useful for improving loading speed.
Box 2: Clustered columnstore –
When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed.
Box 3: Date –
Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column.
Partition switching can be used to quickly remove or replace a section of a table.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partition
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute

Suggested Answer: See the explanation below.

Create a general-purpose v2 storage account, which provides access to all of the Azure Storage services: blobs, files, queues, tables, and disks.
1. On the Azure portal menu, select All services. In the list of resources, type Storage Accounts. As you begin typing, the list filters based on your input. Select
Storage Accounts.
2. On the Storage Accounts window that appears, choose Add.
3. Select the subscription in which to create the storage account.
4. Under the Resource group field, select Create new. Enter the name for your new resource group, as shown in the following image.

5. Next, enter the name account10543936 for your storage account.
6. Select a location for your storage account, or use the default location.
7. Leave these fields set to their default values:
Deployment model: Resource Manager
Performance: Standard –
Account kind: StorageV2 (general-purpose v2)
Replication: Read-access geo-redundant storage (RA-GRS)
Access tier: Hot –
8. Select Review + Create to review your storage account settings and create the account.
9. Select Create.
Reference: alt=”Reference Image” />
5. Next, enter the name account10543936 for your storage account.
6. Select a location for your storage account, or use the default location.
7. Leave these fields set to their default values:
Deployment model: Resource Manager
Performance: Standard –
Account kind: StorageV2 (general-purpose v2)
Replication: Read-access geo-redundant storage (RA-GRS)
Access tier: Hot –
8. Select Review + Create to review your storage account settings and create the account.
9. Select Create.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-account-create

Suggested Answer:

Box 1: memory-optimized table –
In-Memory OLTP can provide great performance benefits for transaction processing, data ingestion, and transient data scenarios.
Box 2: materialized view –
To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The
Materialized View pattern describes generating prepopulated views of data in environments where the source data isn’t in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.
These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view

Suggested Answer: A

We need one reference data input for LocationIncomes, which rarely changes.
We need two queries, on for in-store customers, and one for online customers.
For each query two outputs is needed.
Note: Stream Analytics also supports input known as reference data. Reference data is either completely static or changes slowly.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-add-inputs#stream-and-reference-inputs
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs

Suggested Answer: BF

Scale out the query by allowing the system to process each input partition separately.
F: A Stream Analytics job definition includes inputs, a query, and output. Inputs are where the job reads the data stream from.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization

Suggested Answer: C

When creating a schedule trigger, you specify a schedule (start date, recurrence, end date etc.) for the trigger, and associate with a Data Factory pipeline.
Scenario:
All data migration processes must use Azure Data Factory
All data migrations must run automatically during non-business hours
References:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-schedule-trigger