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MLA-C01 — questions

Page 6 of 12 · 226 total questions.

MLA-C01 Question 101

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Topic 1 · Question 101

A company needs to extract entities from a PDF document to build a classifier model. Which solution will extract and store the entities in the LEAST amount of time?

  • AUse Amazon Comprehend to extract the entities. Store the output in Amazon S3.
  • BUse an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
  • CUse Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3. (correct answer)
  • DUse Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon Comprehend extracts insights and entities from text using NLP. Amazon Textract extracts text and data from scanned documents.

MLA-C01 Question 102

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Topic 1 · Question 102

A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN. The company must enforce access controls to prevent malicious actors from exploiting presigned URLs to access the notebooks. Which solution will meet these requirements?

  • ASet up Studio client IP validation by using the aws:sourceIp IAM policy condition. (correct answer)
  • BSet up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
  • CSet up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.
  • DSet up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.

Explanation

AWS IAM controls authenticated and authorized access to AWS resources with fine-grained policies.

MLA-C01 Question 103

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Topic 1 · Question 103

An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation. The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time. What should the ML engineer do to confirm or deny this hypothesis?

  • ASchedule a SageMaker Model Monitor job. Observe metrics about model quality.
  • BSchedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
  • CEnable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
  • DEnable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLA-C01 Question 104

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Topic 1 · Question 104

A company must install a custom script on any newly created Amazon SageMaker notebook instances. Which solution will meet this requirement with the LEAST operational overhead?

  • ACreate a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps. (correct answer)
  • BCreate a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker notebook.
  • CCreate a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker instance. Install the script.
  • DStore the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker notebook is initialized.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Create a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 105

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Topic 1 · Question 105

A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processed, and visualized in near real time. The company needs a solution that supports SQL for data processing and Jupyter notebooks for interactive analysis. Which solution will meet these requirements?

  • AUse Amazon Data Firehose to ingest the data. Create an AWS Lambda function to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
  • BUse Amazon Kinesis Data Streams to ingest the data. Use Amazon Data Firehose to transform the data. Use Amazon Athena to process the data. Use Amazon QuickSight to visualize the data.
  • CUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use AWS Glue with PySpark to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
  • DUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use Amazon Managed Service for Apache Flink to process the data. Use the built-in Flink dashboard to visualize the data. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use Amazon Managed Service for Apache Flink to process the data. Use the built-in Flink dashboard to visualize the data.

Explanation

Amazon MSK provides fully managed Apache Kafka for streaming. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 106

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Topic 1 · Question 106

A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI). An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models. Which solution will meet these requirements?

  • AStore the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.
  • BUpload the clinical data to an Amazon Redshift database. Use built-in SQL stored procedures to automatically classify and mask the PII and PHI before the data is used for model training.
  • CUse Amazon Comprehend to detect and mask the PII before the data is used for model training. Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training. (correct answer)
  • DCreate an AWS Lambda function to encrypt the PII and PHI. Program the Lambda function to save the encrypted data to an Amazon S3 bucket for model training.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon Comprehend to detect and mask the PII before the data is used for model training. Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training.

Explanation

Amazon Comprehend extracts insights and entities from text using NLP.

MLA-C01 Question 107

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Topic 1 · Question 107

A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN. The company must enforce access controls to prevent malicious actors from exploiting presigned URLs to access the notebooks. Which solution will meet these requirements?

  • ASet up Studio client IP validation by using the aws:sourceIp IAM policy condition. (correct answer)
  • BSet up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
  • CSet up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.
  • DSet up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.

Explanation

AWS IAM controls authenticated and authorized access to AWS resources with fine-grained policies.

MLA-C01 Question 108

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Topic 1 · Question 108

An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Each .csv file consists of millions of records. The other data source is an Amazon Aurora DB cluster. The result of the merge process must be written to a second S3 bucket. The ML engineer needs to perform this merge-and-transform task every week. Which solution will meet these requirements with the LEAST operational overhead?

  • ACreate a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.
  • BCreate a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data. (correct answer)
  • CCreate an AWS Lambda function that runs Apache Spark code every week to merge and transform the data. Configure the Lambda function to connect to the initial S3 bucket and the DB cluster.
  • DCreate an AWS Batch job that runs Apache Spark code on Amazon EC2 instances every week. Configure the Spark code to save the data from the EC2 instances to the second S3 bucket.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.

Explanation

AWS Glue is a serverless ETL and data-catalog service that prepares and integrates data.

MLA-C01 Question 109

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Topic 1 · Question 109

An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation. The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time. What should the ML engineer do to confirm or deny this hypothesis?

  • ASchedule a SageMaker Model Monitor job. Observe metrics about model quality.
  • BSchedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
  • CEnable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
  • DEnable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLA-C01 Question 110

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Topic 1 · Question 110

An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Amazon SageMaker instances. SageMaker must not use any of the PII. Which solution will meet these requirements in the MOST operationally efficient way?

  • AUse the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon S3 bucket. Access the S3 bucket from the SageMaker instances for model training. (correct answer)
  • BUse the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
  • CUse AWS Glue DataBrew to cleanse the dataset of PII. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
  • DUse Amazon Macie for automatic discovery of PII in the data. Remove the PII. Store the data in an Amazon S3 bucket. Mount the S3 bucket to the SageMaker instances for model training.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon S3 bucket. Access the S3 bucket from the SageMaker instances for model training.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon Comprehend extracts insights and entities from text using NLP.

MLA-C01 Question 111

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Topic 1 · Question 111

A company must install a custom script on any newly created Amazon SageMaker notebook instances. Which solution will meet this requirement with the LEAST operational overhead?

  • ACreate a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps. (correct answer)
  • BCreate a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker notebook.
  • CCreate a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker instance. Install the script.
  • DStore the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker notebook is initialized.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Create a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 112

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Topic 1 · Question 112

A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processed, and visualized in near real time. The company needs a solution that supports SQL for data processing and Jupyter notebooks for interactive analysis. Which solution will meet these requirements?

  • AUse Amazon Data Firehose to ingest the data. Create an AWS Lambda function to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
  • BUse Amazon Kinesis Data Streams to ingest the data. Use Amazon Data Firehose to transform the data. Use Amazon Athena to process the data. Use Amazon QuickSight to visualize the data.
  • CUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use AWS Glue with PySpark to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
  • DUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use Amazon Managed Service for Apache Flink to process the data. Use the built-in Flink dashboard to visualize the data. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use Amazon Managed Service for Apache Flink to process the data. Use the built-in Flink dashboard to visualize the data.

Explanation

Amazon MSK provides fully managed Apache Kafka for streaming. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 113

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Topic 1 · Question 113

A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI). An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models. Which solution will meet these requirements?

  • AStore the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.
  • BUpload the clinical data to an Amazon Redshift database. Use built-in SQL stored procedures to automatically classify and mask the PII and PHI before the data is used for model training.
  • CUse Amazon Comprehend to detect and mask the PII before the data is used for model training. Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training. (correct answer)
  • DCreate an AWS Lambda function to encrypt the PII and PHI. Program the Lambda function to save the encrypted data to an Amazon S3 bucket for model training.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon Comprehend to detect and mask the PII before the data is used for model training. Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training.

Explanation

Amazon Comprehend extracts insights and entities from text using NLP.

MLA-C01 Question 114

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Topic 1 · Question 114

An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker. Which solution will provide this functionality with the LEAST implementation effort?

  • AManually install the libraries in the SageMaker containers.
  • BBuild a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker jobs and pipelines. (correct answer)
  • CCreate a SageMaker notebook instance to host the jobs. Create an AWS Lambda function to install the libraries on the notebook instance when the notebook instance starts. Configure the SageMaker jobs and pipelines to run on the notebook instance.
  • DRun code for the libraries externally on Amazon EC2 instances. Store the results in Amazon S3. Import the results into the SageMaker jobs and pipelines.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker jobs and pipelines.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLA-C01 Question 115

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Topic 1 · Question 115

An ML engineer is deploying a trained model to an Amazon SageMaker endpoint. The ML engineer needs to receive alerts when data quality issues occur in production. Which solution will meet this requirement?

  • AConfigure an Amazon CloudWatch metric alarm and a corresponding action to send an Amazon Simple Notification Service (Amazon SNS) notification.
  • BIntegrate the SageMaker endpoint with a SageMaker Clarify processing job. Configure an Amazon CloudWatch alarm to provide alerts.
  • CConfigure a monitoring job in SageMaker Model Monitor. Integrate Model Monitor with Amazon CloudWatch to provide alerts. (correct answer)
  • DConfigure a data flow in SageMaker Data Wrangler. Integrate Data Wrangler with Amazon CloudWatch to provide alerts.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Configure a monitoring job in SageMaker Model Monitor. Integrate Model Monitor with Amazon CloudWatch to provide alerts.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health. AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 116

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Topic 1 · Question 116

A company needs to use Amazon SageMaker to train a model on more than 300 GB of data. The training data is composed of files that are 200 MB in size. The data is stored in Amazon S3 Standard storage and feeds a dashboard tool. Which SageMaker training ingestion mechanism is the MOST cost-effective solution for this scenario?

  • AAmazon Elastic File System (Amazon EFS) file system
  • BAmazon FSx for Lustre file system
  • CAmazon S3 in fast file mode while using S3 Express One Zone
  • DAmazon S3 in fast file mode without using S3 Express One Zone (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Amazon S3 in fast file mode without using S3 Express One Zone

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. This option delivers the requirement at the lowest cost.

MLA-C01 Question 117

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Topic 1 · Question 117

A company has an ML model that is deployed to an Amazon SageMaker endpoint for real-time inference. The company needs to deploy a new model. The company must compare the new model’s performance to the currently deployed model’s performance before shifting all traffic to the new model. Which solution will meet these requirements with the LEAST operational effort?

  • ADeploy the new model to a separate endpoint. Manually split traffic between the two endpoints.
  • BDeploy the new model to a separate endpoint. Use Amazon CloudFront to distribute traffic between the two endpoints.
  • CDeploy the new model as a shadow variant on the same endpoint as the current model. Route a portion of live traffic to the shadow model for evaluation. (correct answer)
  • DUse AWS Lambda functions with custom logic to route traffic between the current model and the new model.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Deploy the new model as a shadow variant on the same endpoint as the current model. Route a portion of live traffic to the shadow model for evaluation.

MLA-C01 Question 118

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Topic 1 · Question 118

A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker from collecting metadata from the training jobs?

  • AOpt out of metadata tracking for any training job that is submitted. (correct answer)
  • BEnsure that training jobs are running in a private subnet in a custom VPC.
  • CEncrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key.
  • DReconfigure the training jobs to use only AWS Nitro instances.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Opt out of metadata tracking for any training job that is submitted.

MLA-C01 Question 119

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Topic 1 · Question 119

A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock. The EC2 instances are in a private subnet and must remain private during the implementation. The EC2 instances have an assigned security group that allows access to all IP addresses in the private subnet. What should the ML engineer do to establish a connection between the EC2 instances and Amazon Bedrock?

  • AModify the security group to allow inbound and outbound traffic to and from Amazon Bedrock.
  • BUse AWS PrivateLink to access Amazon Bedrock through an interface VPC endpoint. (correct answer)
  • CConfigure Amazon Bedrock to use the private subnet where the EC2 instances are deployed.
  • DLink the existing VPC to Amazon Bedrock by using an AWS Direct Connect connection.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Use AWS PrivateLink to access Amazon Bedrock through an interface VPC endpoint.

Explanation

A VPC endpoint provides private connectivity to AWS services without traversing the public internet. AWS PrivateLink exposes services privately inside the VPC without internet exposure. Amazon Bedrock provides access to foundation models through a managed API.

MLA-C01 Question 120

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Topic 1 · Question 120 · Select all that apply

A company wants to launch a new internal generative AI interface to answer user questions. The interface will be based on a popular open source large language model (LLM). Which combination of steps will deploy the interface with the LEAST operational overhead? (Choose two.)

  • AUse Amazon SageMaker JumpStart to deploy the LLM. (correct answer)
  • BDownload the LLM as a .zip file. Deploy the LLM on a GPU-based Amazon EC2 instance.
  • CCreate a frontend HTML interface that uses an Amazon API Gateway WebSocket API with AWS Lambda functions to handle the user interaction.
  • DUse Amazon QuickSight to create a UI to handle the user interaction.
  • EUse Amazon Lex to create a UI to handle the user interaction. (correct answer)
Reveal answer & explanation
Correct answer: A, E

The correct answer is A, E. Option A: Use Amazon SageMaker JumpStart to deploy the LLM. Option E: Use Amazon Lex to create a UI to handle the user interaction.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

Showing questions 101–120 of 226 · Page 6 of 12