A company is building a pipeline that periodically retrains its machine learning (ML) models by using new streaming data from devices. The company's data engineering team wants to build a data ingestion system that has high throughput, durable storage, and scalability. The company can tolerate up to 5 minutes of latency for data ingestion. The company needs a solution that can apply basic data transformation during the ingestion process.
Which solution will meet these requirements with the MOST operational efficiency?

A. Configure the devices to send streaming data to an Amazon Kinesis data stream. Configure an Amazon Kinesis Data Firehose delivery stream to automatically consume the Kinesis data stream, transform the data with an AWS Lambda function, and save the output into an Amazon S3 bucket.

B. Configure the devices to send streaming data to an Amazon S3 bucket. Configure an AWS Lambda function that is invoked by S3 event notifications to transform the data and load the data into an Amazon Kinesis data stream. Configure an Amazon Kinesis Data Firehose delivery stream to automatically consume the Kinesis data stream and load the output back into the S3 bucket.

C. Configure the devices to send streaming data to an Amazon S3 bucket. Configure an AWS Glue job that is invoked by S3 event notifications to read the data, transform the data, and load the output into a new S3 bucket.

D. Configure the devices to send streaming data to an Amazon Kinesis Data Firehose delivery stream. Configure an AWS Glue job that connects to the delivery stream to transform the data and load the output into an Amazon S3 bucket.

A Data Engineer needs to build a model using a dataset containing customer credit card information
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

A Machine Learning Specialist needs to move and transform data in preparation for training. Some of the data needs to be processed in near-real time, and other data can be moved hourly. There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.
Which of the following services can feed data to the MapReduce jobs? (Choose two.)

A. AWS DMS

B. Amazon Kinesis

C. AWS Data Pipeline

D. Amazon Athena

E. Amazon ES

A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:
TransactionTimestamp (Timestamp)
CardName (Varchar)
CardNo (Varchar)
The data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a TransactionTime column. Finally, the CardName column must be renamed to NameOnCard.
The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution also must be automated and must minimize the load on the Amazon Redshift cluster.
Which solution meets these requirements?

A. Set up an Amazon EMR cluster. Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.

B. Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J, to query the data from the Amazon Redshift cluster directly Export the resulting dataset into a file. Upload the file into the S3 bucket. Perform these tasks monthly.

C. Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination. Use the built-in transforms Filter, Map, and RenameField to perform the required transformations. Schedule the job to run monthly.

D. Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly.

A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?

A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.

B. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.

C. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.

D. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

A newspaper publisher has a table of customer data that consists of several numerical and categorical features, such as age and education history, as well as subscription status. The company wants to build a targeted marketing model for predicting the subscription status based on the table data.
Which Amazon SageMaker built-in algorithm should be used to model the targeted marketing?

A. Random Cut Forest (RCF)

B. XGBoost

C. Neural Topic Model (NTM)

D. DeepAR forecasting

An online retail company wants to develop a natural language processing (NLP) model to improve customer service. A machine learning (ML) specialist is setting up distributed training of a Bidirectional Encoder Representations from Transformers (BERT) model on Amazon SageMaker. SageMaker will use eight compute instances for the distributed training.
The ML specialist wants to ensure the security of the data during the distributed training. The data is stored in an Amazon S3 bucket.
Which combination of steps should the ML specialist take to protect the data during the distributed training? (Choose three.)

A. Run distributed training jobs in a private VPC. Enable inter-container traffic encryption.

B. Run distributed training jobs across multiple VPCs. Enable VPC peering.

C. Create an S3 VPC endpoint. Then configure network routes, endpoint policies, and S3 bucket policies.

D. Grant read-only access to SageMaker resources by using an IAM role.

E. Create a NAT gateway. Assign an Elastic IP address for the NAT gateway.

F. Configure an inbound rule to allow traffic from a security group that is associated with the training instances.

A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.
Which combination of services can be used to build this conversational interface? (Choose three.)

A. Alexa for Business

B. Amazon Connect

C. Amazon Lex

D. Amazon Polly

E. Amazon Comprehend

F. Amazon Transcribe

A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

A. Latent Dirichlet allocation (LDA)

B. Random forest classifier

C. Neural topic modeling (NTM)

D. Linear support vector machine

E. Linear regression

A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3.
The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.
Which combination of solutions will meet these requirements with the LEAST operational overhead? (Choose three.)

A. Use Amazon Athena to scan the data and identify the schema.

B. Use AWS Glue crawlers to scan the data and identify the schema.

C. Use Amazon Redshift to store procedures to perform data transformations.

D. Use AWS Glue workflows and AWS Glue jobs to perform data transformations.

E. Use Amazon Redshift ML to train a model to detect fraud.

F. Use Amazon Fraud Detector to train a model to detect fraud.

A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time.
Which combination of steps is the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Choose two.)

A. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.

B. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.

C. Store the model predictions in Amazon S3. Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.

D. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.

E. Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.

A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.
A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.
Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?

A. Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.

B. Use the SageMaker BlazingText algorithm.

C. Use the SageMaker Neural Topic Model (NTM) algorithm.

D. Use the SageMaker CatBoost algorithm.

A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ׀¢׀’ of training data that consists of labeled images of defective product parts. The training data is in the corporate on- premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.

B. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.

C. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

D. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.
A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon QuickSight dashboard to display near real-time order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.
Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

A. Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure QuickSight to access the data in Amazon S3.

B. Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3. Configure QuickSight to access the data in Amazon S3.

C. Use an API call from QuickSight to access the data that is in Amazon DynamoDB directly.

D. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure QuickSight to access the data in Amazon S3.

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.
Which tool should be used to improve the validation accuracy?

A. Amazon Comprehend syntax analysis and entity detection

B. Amazon SageMaker BlazingText cbow mode

C. Natural Language Toolkit (NLTK) stemming and stop word removal

D. Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer

An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model for each city where the company operates.
Operation engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory. The operation engineers want to avoid managing unnecessary resources.
Which solution will enable the company to achieve its goal with the LEAST operational overhead?

A. Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.

B. Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models. Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.

C. Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.

D. Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints, one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpointName parameter according to the city of each request.

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?

A. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.

B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.

C. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.

D. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to make changes to applications that rely on the API. The company also wants to evaluate the performance of the new model in production traffic before the company fully rolls out the new model to all users.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a new SageMaker endpoint for the new model. Configure an Application Load Balancer (ALB) to distribute traffic between the old model and the new model.

B. Modify the existing endpoint to use SageMaker production variants to distribute traffic between the old model and the new model.

C. Modify the existing endpoint to use SageMaker batch transform to distribute traffic between the old model and the new model.

D. Create a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB) to distribute traffic between the old model and the new model.

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.

B. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.

C. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.

A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?

A. Set up a 10 Gbps AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images. Increase the size of the instances and the number of instances that are used by the SageMaker endpoint.

B. Extend the long-running Lambda function that runs on AWS IoT Greengrass to compress the images and upload the compressed files to Amazon S3. Decompress the files by using a separate Lambda function that invokes the existing Lambda function to run the inference pipeline.

C. Use auto scaling for SageMaker. Set up an AWS Direct Connect connection between the production site and the nearest AWS Region. Use the Direct Connect connection to upload the images.

D. Deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machine. Extend the long-running Lambda function that runs on AWS IoT Greengrass to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service.

A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.
 
How should the data scientist transform the data?

A. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.

C. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.

D. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

A data scientist is working on a model to predict a company's required inventory stock levels. All historical data is stored in .csv files in the company's data lake on Amazon S3. The dataset consists of approximately 500 GB of data The data scientist wants to use SQL to explore the data before training the model. The company wants to minimize costs.
Which option meets these requirements with the LEAST operational overhead?

A. Create an Amazon EMR cluster. Create external tables in the Apache Hive metastore, referencing the data that is stored in the S3 bucket. Explore the data from the Hive console.

B. Use AWS Glue to crawl the S3 bucket and create tables in the AWS Glue Data Catalog. Use Amazon Athena to explore the data.

C. Create an Amazon Redshift cluster. Use the COPY command to ingest the data from Amazon S3. Explore the data from the Amazon Redshift query editor GUI.

D. Create an Amazon Redshift cluster. Create external tables in an external schema, referencing the S3 bucket that contains the data. Explore the data from the Amazon Redshift query editor GUI.

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time.
Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

A. Streaming classification

B. Binary classification

C. Multi-category classification

D. Regression classification

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. The training channel identifying the location of training data on an Amazon S3 bucket.

B. The validation channel identifying the location of validation data on an Amazon S3 bucket.

C. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

D. Hyperparameters in a JSON array as documented for the algorithm used.

E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.
A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.
Which labeling approach will help the company improve this model?

A. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

B. Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

C. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

D. Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

A news company is developing an article search tool for its editors. The search tool should look for the articles that are most relevant and representative for particular words that are queried among a corpus of historical news documents.
The editors test the first version of the tool and report that the tool seems to look for word matches in general. The editors have to spend additional time to filter the results to look for the articles where the queried words are most important. A group of data scientists must redesign the tool so that it isolates the most frequently used words in a document. The tool also must capture the relevance and importance of words for each document in the corpus.
Which solution meets these requirements?

A. Extract the topics from each article by using Latent Dirichlet Allocation (LDA) topic modeling. Create a topic table by assigning the sum of the topic counts as a score for each word in the articles. Configure the tool to retrieve the articles where this topic count score is higher for the queried words.

B. Build a term frequency for each word in the articles that is weighted with the article’s length. Build an inverse document frequency for each word that is weighted with all articles in the corpus. Define a final highlight score as the product of both of these frequencies. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.

C. Download a pretrained word-embedding lookup table. Create a titles-embedding table by averaging the title’s word embedding for each article in the corpus. Define a highlight score for each word as inversely proportional to the distance between its embedding and the title embedding. Configure the tool to retrieve the articles where this highlight score is higher for the queried words.

D. Build a term frequency score table for each word in each article of the corpus. Assign a score of zero to all stop words. For any other words, assign a score as the word’s frequency in the article. Configure the tool to retrieve the articles where this frequency score is higher for the queried words.

A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure,` and "documentary.`
A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

A. Add the unrecognized words in the enumeration values list as new values in the slot type.

B. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.

C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.

D. Add the unrecognized words as synonyms in the custom slot type.

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Choose two.)

A. Use Amazon SageMaker Ground Truth to sort the data into two groups named “enrolled” or “not enrolled.”

B. Use a forecasting algorithm to run predictions.

C. Use a regression algorithm to run predictions.

D. Use a classification algorithm to run predictions.

E. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named “enrolled” or “not enrolled.”

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve
(AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

A. A histogram showing whether the most important input feature is Gaussian.

B. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.

C. A scatter plot showing the performance of the objective metric over each training iteration.

D. A scatter plot showing the correlation between maximum tree depth and the objective metric.

An exercise analytics company wants to predict running speeds for its customers by using a dataset that contains multiple health-related features for each customer. Some of the features originate from sensors that provide extremely noisy values.
The company is training a regression model by using the built-in Amazon SageMaker linear learner algorithm to predict the running speeds. While the company is training the model, a data scientist observes that the training loss decreases to almost zero, but validation loss increases.
Which technique should the data scientist use to optimally fit the model?

A. Add L1 regularization to the linear learner regression model.

B. Perform a principal component analysis (PCA) on the dataset. Use the linear learner regression model.

C. Perform feature engineering by including quadratic and cubic terms. Train the linear learner regression model.

D. Add L2 regularization to the linear learner regression model.

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

A. Autoregressive Integrated Moving Average (AIRMA)

B. Exponential Smoothing (ETS)

C. Convolutional Neural Network – Quantile Regression (CNN-QR)

D. Prophet

A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
✑ Profiles for all past and existing customers
✑ Profiles for all past and existing insured pets
✑ Policy-level information
✑ Premiums received
✑ Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

B. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

D. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

A. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.

B. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

C. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.

D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company’s stores across five commercial regions. The data scientist creates a working dataset with StoreID. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions.
Which visualization will help the data scientist better understand the data trend?

A. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, faceted by year, of average sales for each store. Add an extra bar in each facet to represent average sales.

B. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each store. Create a bar plot, colored by region and faceted by year, of average sales for each store. Add a horizontal line in each facet to represent average sales.

C. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot of average sales for each region. Add an extra bar in each facet to represent average sales.

D. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region. Create a bar plot, faceted by year, of average sales for each region. Add a horizontal line in each facet to represent average sales.

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near- real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

A. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.

B. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.

C. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.

D. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

A. Choose a higher number of layers

B. Choose a lower number of layers

C. Choose a smaller learning rate

D. Enable dropout

E. Include all the images from the test set in the training set

F. Enable early stopping

A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU:GPU ratio of 12:1 to train the models.
The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time. The ML specialist must reduce training costs without increasing the duration of the training jobs.
Which solution will meet these requirements?

A. Switch to an instance type that has only CPUs.

B. Use a heterogeneous cluster that has two different instances groups.

C. Use memory-optimized EC2 Spot Instances for the training jobs.

D. Switch to an instance type that has a CPU:GPU ratio of 6:1.

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones
(negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

A. Specificity

B. False positive rate

C. Accuracy

D. Area under the precision-recall curve

E. True positive rate

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

A. Root Mean Square Error (RMSE)

B. Residual plots

C. Area under the curve

D. Confusion matrix

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end- to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?

A. Write a direct connection to the SQL database within the notebook and pull data in

B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.

D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

A data engineer is evaluating customer data in Amazon SageMaker Data Wrangler. The data engineer will use the customer data to create a new model to predict customer behavior.
The engineer needs to increase the model performance by checking for multicollinearity in the dataset.
Which steps can the data engineer take to accomplish this with the LEAST operational effort? (Choose two.)

A. Use SageMaker Data Wrangler to refit and transform the dataset by applying one-hot encoding to category-based variables.

B. Use SageMaker Data Wrangler diagnostic visualization. Use principal components analysis (PCA) and singular value decomposition (SVD) to calculate singular values.

C. Use the SageMaker Data Wrangler Quick Model visualization to quickly evaluate the dataset and to produce importance scores for each feature.

D. Use the SageMaker Data Wrangler Min Max Scaler transform to normalize the data.

E. Use SageMaker Data Wrangler diagnostic visualization. Use least absolute shrinkage and selection operator (LASSO) to plot coefficient values from a LASSO model that is trained on the dataset.

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also shows a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

A. Cross-validation

B. Numerical value binning

C. High-degree polynomial transformation

D. Logarithmic transformation

E. One hot encoding

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?

A. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.

B. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.

C. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.

D. Remove the stopwords from the blog post data by using the CountVectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.

A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.
How should the data scientist accomplish this?

A. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.

B. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.

C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.

D. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

A sports broadcasting company is planning to introduce subtitles in multiple languages for a live broadcast. The commentary is in English. The company needs the transcriptions to appear on screen in French or Spanish, depending on the broadcasting country. The transcriptions must be able to capture domain-specific terminology, names, and locations based on the commentary context. The company needs a solution that can support options to provide tuning data.
Which combination of AWS services and features will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Amazon Transcribe with custom vocabularies

B. Amazon Transcribe with custom language models

C. Amazon SageMaker Seq2Seq

D. Amazon SageMaker with Hugging Face Speech2Text

E. Amazon Translate

A company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each larger than 200 MB in size. The company needs a data access solution that offers the shortest processing time and the least amount of setup.
Which solution will meet these requirements?

A. Use File mode in SageMaker to copy the dataset from the S3 buckets to the ML instance storage.

B. Create an Amazon FSx for Lustre file system. Link the file system to the S3 buckets.

C. Create an Amazon Elastic File System (Amazon EFS) file system. Mount the file system to the training instances.

D. Use FastFile mode in SageMaker to stream the files on demand from the S3 buckets.

A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.
Which solution will meet this requirement with the LEAST development effort?

A. Use SageMaker Data Wrangler to perform a Gini importance score analysis.

B. Use a SageMaker notebook instance to perform principal component analysis (PCA).

C. Use a SageMaker notebook instance to perform a singular value decomposition analysis.

D. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint. However, when the data scientist attempts to invoke the SageMaker endpoint, the data scientist receives SQL statement failures. The data scientist’s IAM user is currently unable to invoke the SageMaker endpoint.
Which combination of actions will give the data scientist’s IAM user the ability to invoke the SageMaker endpoint? (Choose three.)

A. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.

B. Include a policy statement for the data scientist’s IAM user that allows the IAM user to perform the sagemaker:InvokeEndpoint action.

C. Include an inline policy for the data scientist’s IAM user that allows SageMaker to read S3 objects.

D. Include a policy statement for the data scientist’s IAM user that allows the IAM user to perform the sagemaker:GetRecord action.

E. Include the SQL statement “USING EXTERNAL FUNCTION ml_function_name” in the Athena SQL query.

F. Perform a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint.

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company. Most of the data in the training dataset is missing values for the target variable. The company stores the training dataset as JSON files.
The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset.
Which approach will meet these requirements with the LEAST development effort?

A. Impute the missing values by using the linear regression method. Use the entire dataset and the imputed values to train the DeepAR model.

B. Replace the missing values with not a number (NaN). Use the entire dataset and the encoded missing values to train the DeepAR model.

C. Impute the missing values by using a forward fill. Use the entire dataset and the imputed values to train the DeepAR model.

D. Impute the missing values by using the mean value. Use the entire dataset and the imputed values to train the DeepAR model.

A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain.
The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain.
The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department.
How should the company meet these requirements?

A. Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.

B. Use the SageMaker CreateHumanTaskUi API to generate a UI URL. Pass the URL to the proxy application.

C. Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.

D. Use the SageMaker CreatePresignedNotebooklnstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.