QUESTION 41
A company is developing a generative Al conversational interface to assist customers with payments. The company wants to use an ML solution to detect customer intent. The company does not have training data to train a model. Which solution will meet these requirements?

A. Fine-tune a sequence-to-sequence (seq2seq) algorithm in Amazon SageMaker JumpStart.
B. Use an LLM from Amazon Bedrock with zero-shot learning.
C. Use the Amazon Comprehend DetectEntities API.
D. Run an LLM from Amazon Bedrock on Amazon EC2 instances.

Correct Answer: B

QUESTION 42
A company wants to use AWS to deploy a large language model (LLM) agent for a model that is already trained. The company needs the capability to answer questions and provide updates about orders that are stored in an Amazon DynamoDB table. Which solution will meet these requirements?

A. Use Amazon SageMaker JumpStart to deploy an LLM endpoint. Use AWS Lambda as an agent orchestrator and to retrieve order data from DynamoDB.
B. Dye Amazon SageMaker Serverless Inference to deploy an LLM endpoint. Use Amazon Bedrock as an agent orchestrator and to retrieve order data from DynamoDB.
C. Use Amazon Elastic Container Service (Amazon ECS) to deploy an LLM endpoint. Use AWS Fargate as an agent orchestrator and to retrieve order data from DynamoDB.
D. Use Amazon Elastic Kubernetes Service (Amazon EKS) to deploy an LLM endpoint. Use an Amazon Bedrock agent as an agent orchestrator and to retrieve order data from DynamoDB.

Correct Answer: B

QUESTION 43
A company collects customer data every day. The company stores the data as compressed files in an Amazon S3 bucket that is partitioned by date. Every month, analysts download the data, process the data to check the data quality, and then upload the data to Amazon QuickSight dashboards. An ML engineer needs to implement a solution to automatically check the data quality before the data is sent to QuickSight. Which solution will meet these requirements with the LEAST operational overhead?

A. Run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Use AWS Glue Data Quality rules to check the data quality.
B. Use an AWS Glue trigger to run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Create an AWS Glue job that loads the data into a PySpark DataFrame. Configure the job to apply custom functions and to evaluate the data quality.
C. Run Python scripts on an AWS Lambda function every month to evaluate data quality. Configure the S3 bucket to invoke the Lambda function when objects are added to the S3 bucket.
D. Configure the S3 bucket to send event notifications to an Amazon Simple Queue Service (Amazon SQS) queue when objects are uploaded. Use Amazon CloudWatch insights every month for the SQS queue to evaluate the data quality.

Correct Answer: A

QUESTION 44
A bank needs to use Amazon SageMaker AI to create an ML model to determine which customers qualify for a new product. The bank must use algorithms that SageMaker AI directly supports. The model must be explainable to the bank’s regulators. Which modeling approach will meet these requirements?

A. Train the model by using the Object2Vec algorithm.
B. Train the model by using the linear learner algorithm.
C. Train a neural network.
D. Train the model by using the k-means algorithm.

Correct Answer: B

QUESTION 45
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts. An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources. Which solution will meet these requirements with the LEAST development effort?

A. Create code to evaluate each instance’s memory and compute usage.
B. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
C. Check AWS CloudTrail event history for the creation of the resources.
D. Run AWS Compute Optimizer.

Correct Answer: D

QUESTION 46
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company’s Amazon S3 bucket even. The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket. Which solution will meet these requirements with the LEAST operational effort?

A. Create an S3 Lifecycle rule to transfer the data to the SageMaker AI training instance and to initiate training.
B. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
C. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.

Correct Answer: C

QUESTION 47
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold. Which solution will meet these requirements?

A. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
B. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.
C. Add resource tagging by editing each user’s IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
D. Add resource tagging by editing each user’s IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.

Correct Answer: B

QUESTION 48
An ML engineer is setting up a continuous integration and continuous delivery (CI/CD) pipeline for an ML workflow in Amazon SageMaker AI. The pipeline needs to automate model re-training, testing, and deployment whenever new data is uploaded to an Amazon S3 bucket. New data files are approximately 10 GB in size. The ML engineer wants to track model versions for auditing. Which solution will meet these requirements?

A. Use AWS CodePipeline, Amazon S3, and AWS CodeBuild to retrain and deploy the model automatically and to track model versions.
B. Use SageMaker Pipelines with the SageMaker Model Registry to orchestrate model training and version tracking.
C. Create an AWS Lambda function to re-train and deploy the model. Use Amazon EventBridge to invoke the Lambda function. Reference the Lambda logs to track model versions.
D. Use SageMaker AI notebook instances to manually re-train and deploy the model when needed. Reference AWS CloudTrail logs to track model versions.

Correct Answer: B

QUESTION 49
A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store. The company needs a hybrid system to make the shared data store accessible to on-premises servers and Amazon SageMaker Al notebooks that will consume the data. File locking is required for the data producers. Which AWS storage solution will meet these requirements?

A. Use an Amazon S3 bucket to store the data. Use Mountpoint for Amazon S3 to mount the S3 bucket to the on-premises servers and the SageMaker AI notebooks.
B. Use an Amazon Elastic File System (Amazon EFS) file system to store the data. Mount the file system to the on-premises servers and the SageMaker AI notebooks.
C. Use an Amazon FSx for Lustre file system to store the data. Mount the file system to the on-premises servers and the SageMaker AI notebooks.
D. Use an Amazon Elastic Block Store (Amazon EBS) volume to store the data. Mount the volume to the on-premises servers and the SageMaker Al notebooks.

Correct Answer: B

QUESTION 50
An ML engineer stores structured data in a single 16-TB General Purpose SSD (gp3) Amazon Elastic Block Store (Amazon EBS) volume. The size of the dataset has grown significantly. The ML engineer anticipates a need for a 100-TB storage solution that will provide sub-millisecond read latency. Which solution will meet these requirements?

A. Increase the capacity of the single gp3 EBS volume to 100 TB
B. Change the volume to a single 100-TB Provisioned IOPS SSD (io2) Block Express EBS volume
C. Migrate the data to Amazon S3 standard tier.
D. Migrate the data to Amazon Elastic File System (Amazon EFS) One Zone.

Correct Answer: B

QUESTION 51
A company is planning to use Amazon SageMaker AI to make classification ratings that are based on images. The company has 6 TB of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker AI. An ML engineer must make the training data accessible for ML models that are in the SageMaker AI environment. Which solution will meet these requirements?

A. Mount the FSx for ONTAP file system as a volume to the SageMaker AI instance.
B. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
C. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
D. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

Correct Answer: D

QUESTION 52
An ML engineer has developed a binary classification model outside of Amazon SageMaker AI. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker AI domain. Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Select TWO.)

A. The ML engineer and the Canvas user must be in separate SageMaker AI domains.
B. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
C. The model must be registered in the SageMaker Model Registry.
D. The ML engineer must host the model on AWS Marketplace.
E. The ML engineer must deploy the model to a SageMaker AI endpoint.

Correct Answer: BC

QUESTION 53
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions. Which solution will meet this requirement?

A. Apply statistics from a well-known dataset to normalize the production samples.
B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
C. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
D. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.

Correct Answer: B

QUESTION 54
A company has significantly increased the amount of data that is stored as csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than they used to take. An ML engineer must implement a solution to optimize the data for query performance. Which solution will meet this requirement with the LEAST operational overhead?

A. Configure an AWS Lambda function to split the csv files into smaller objects in the S3 bucket.
B. Configure an AWS Glue job to drop columns that have string type values and to save the results to the S3 bucket.
C. Configure an AWS Glue extract, transform, and load (ETL) job to convert the cs files to Apache Parquet format.
D. Configure an Amazon EMR cluster to process the data that is in the S3 bucket.

Correct Answer: C

QUESTION 55
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket. Which solution will meet these requirements?

A. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
B. Use a custom Amazon SageMaker AI notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
C. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
D. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.

Correct Answer: C

QUESTION 56
A company has a team of data scientists who use Amazon SageMaker AI notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker AI notebook instance. The company needs to centralize management of the team’s permissions. Which solution will meet this requirement?

A. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
C. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
D. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.

Correct Answer: A

QUESTION 57
A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and must display the results on a dashboard. An ML engineer creates a bias monitoring job. How should the ML engineer capture bias metrics to display on the dashboard?

A. Capture AWS CloudTrail metrics from SageMaker Clarify.
B. Capture Amazon CloudWatch metrics from SageMaker Clarity.
C. Capture SageMaker Model Monitor metrics from Amazon EventBridge.
D. Capture SageMaker Model Monitor metrics from Amazon Simple Notification Service (Amazon SNS).

Correct Answer: B

QUESTION 58
A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should the company use to evaluate the models to meet this requirement?

A. F1 score
B. Area Under the ROC Curve (AUC)
C. Precision
D. Recall

Correct Answer: D

QUESTION 59
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second. The company needs to implement a scalable solution on AWS to identify anomalous data points. Which solution will meet these requirements with the LEAST operational overhead?

A. Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
B. Ingest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker AI endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
C. Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker AI endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
D. Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.

Correct Answer: A

QUESTION 60
A company wants to use large language models (LLMs) that are supported by Amazon Bedrock to develop a chat interface for the company’s internal technical documentation. The company stores the documentation as dozens of text files that are several megabytes in total size. The company updates the text files often. Which solution will meet these requirements MOST cost-effectively?

A. Create a new LLM on Amazon Bedrock. Train the new LLM on the original dataset and the company documentation. Make the new model available in Bedrock for calls from the chat interface.
B. Integrate the company documentation with Amazon Bedrock guardrails. Invoke the guardrails for all Amazon Bedrock calls from the chat interface.
C. Use all the text files to fine tune a model in Amazon Bedrock Use the fine-tuned model to process user prompts.
D. Upload all the text files to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when the chat interface makes calls to Amazon Bedrock.

Correct Answer: D