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MLA-C01 — questions

Page 7 of 12 · 226 total questions.

MLA-C01 Question 121

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Topic 1 · Question 121

A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of data each minute. The solution also must load the data into a data store that supports fast queries for the real-time analytics. Which solution will meet these requirements?

  • AUse Amazon EventBridge to ingest the social media data. Use AWS Glue to transform the data. Store the transformed data in Amazon ElastiCache (Memcached).
  • BUse Amazon Simple Queue Service (Amazon SQS) to ingest the social media data. Use AWS Lambda to transform the data. Store the transformed data in Amazon S3.
  • CUse Amazon Simple Notification Service (Amazon SNS) to ingest the social media data. Use Amazon EMR to transform the data. Store the transformed data in Amazon RDS.
  • DUse Amazon Kinesis Data Streams to ingest the social media data. Use Amazon Managed Service for Apache Flink to transform the data. Store the transformed data in Amazon DynamoDB. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use Amazon Kinesis Data Streams to ingest the social media data. Use Amazon Managed Service for Apache Flink to transform the data. Store the transformed data in Amazon DynamoDB.

Explanation

Amazon DynamoDB is a fully managed, serverless NoSQL database with single-digit millisecond latency and automatic scaling. Kinesis Data Streams ingests and processes high-volume real-time streaming data. Amazon Kinesis ingests and processes real-time streaming data at scale. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 122

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Topic 1 · Question 122

A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key. Which solution will meet these requirements?

  • ACreate a new SSH access key. Use the AWS Encryption CLI with a reference to the new access key to encrypt the file.
  • BCreate a new API key by using the Amazon API Gateway CreateApiKey API operation. Use the AWS CLI with a reference to the new API key to encrypt the file.
  • CCreate a new IAM role. Attach a policy that allows the AWS Key Management Service (AWS KMS) GenerateDataKey action. Use the role to encrypt the file.
  • DCreate a new AWS Key Management Service (AWS KMS) key. Use the AWS Encryption CLI with a reference to the new KMS key to encrypt the file. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Create a new AWS Key Management Service (AWS KMS) key. Use the AWS Encryption CLI with a reference to the new KMS key to encrypt the file.

Explanation

AWS KMS manages encryption keys and integrates with most AWS services for encryption at rest.

MLA-C01 Question 123

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Topic 1 · Question 123

A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features. Which solution will meet these requirements?

  • AUse Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features. (correct answer)
  • BUse Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
  • CUse Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
  • DUse an Amazon SageMaker batch transform job to analyze, transform, and ingest the data. Create an Amazon DynamoDB table to store the features.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.

Explanation

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

MLA-C01 Question 124

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Topic 1 · Question 124

A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. The model will use the score to determine which product to display to the customer. The ML engineer needs to minimize response-time latency for the model. How should the ML engineer deploy the application in Amazon SageMaker to meet these requirements?

  • AConfigure batch transform.
  • BConfigure a real-time inference endpoint. (correct answer)
  • CConfigure a serverless inference endpoint.
  • DConfigure an asynchronous inference endpoint.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Configure a real-time inference endpoint.

Explanation

AWS Config tracks resource configuration changes and evaluates compliance.

MLA-C01 Question 125

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Topic 1 · Question 125

A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data. The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing. Which solution will meet these requirements?

  • AUse the PutRecord API in Feature Store Runtime to ingest all the data into the online store.
  • BUse the PutRecord API in Feature Store Runtime to ingest all the data into the offline store.
  • CUse the Feature Store Spark connector to ingest the data as Spark DataFrames with the online store and offline store enabled. (correct answer)
  • DUse the Feature Store Spark connector to ingest the data as Spark DataFrames with only the online store enabled.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use the Feature Store Spark connector to ingest the data as Spark DataFrames with the online store and offline store enabled. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 126

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Topic 1 · Question 126

An ML engineer needs to deploy four ML models in an Amazon SageMaker inference pipeline. The models were built with different frameworks. The ML engineer also needs to give clients the ability to use the invoke_endpoint call to perform inference for each model. Which solution will meet these requirements MOST cost-effectively?

  • ACreate a SageMaker multi-model endpoint.
  • BCreate a SageMaker multi-container endpoint. (correct answer)
  • CCreate multiple SageMaker single-model endpoints.
  • DRun a SparkML job to generate multiple endpoints.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a SageMaker multi-container endpoint.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option delivers the requirement at the lowest cost.

MLA-C01 Question 127

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Topic 1 · Question 127

An ML engineer wants an Amazon SageMaker notebook to automatically stop running after 1 hour of idle time. How can the ML engineer accomplish this goal?

  • ACreate a lifecycle configuration in SageMaker. Copy the auto-stop-idle script from GitHub to the Start Notebook section. (correct answer)
  • BCreate a lifecycle configuration in SageMaker. Copy the auto-stop-idle script from GitHub to the Create Notebook section.
  • CTrack the notebook's CPU metric by using Amazon CloudWatch Logs. Invoke an AWS Lambda function from CloudWatch Logs to shut down the notebook instance if CPU utilization becomes zero.
  • DTrack the notebook's memory metric by using Amazon CloudWatch Logs. Invoke an AWS Lambda function from CloudWatch Logs to shut down the notebook instance if memory utilization becomes zero.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Create a lifecycle configuration in SageMaker. Copy the auto-stop-idle script from GitHub to the Start Notebook section.

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 128

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Topic 1 · Question 128

A company wants to provide services to help other businesses label images. The company wants its labeling specialists to complete human labeling tasks on AWS. How should the company register the labeling specialists to receive tasks on AWS?

  • AUse AWS Data Exchange.
  • BCreate and use an internal workforce in Amazon SageMaker Ground Truth. (correct answer)
  • CCreate and use Amazon Mechanical Turk entities in an Amazon SageMaker human loop.
  • DUse the Amazon Mechanical Turk website.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create and use an internal workforce in Amazon SageMaker Ground Truth.

Explanation

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

MLA-C01 Question 129

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Topic 1 · Question 129

A company wants to use Amazon SageMaker to host an ML model that runs on CPU for real-time predictions. The model will have intermittent traffic during business hours and will have periods of no traffic after business hours. The company needs a solution that will serve inference requests in the most cost-effective manner. Which hosting option will meet these requirements?

  • ADeploy the model to a SageMaker real-time endpoint. Add a schedule-based auto scaling policy to handle traffic surges during business hours.
  • BDeploy the model to a SageMaker Serverless Inference endpoint. Configure increased provisioned concurrency during business hours. (correct answer)
  • CDeploy the model to a SageMaker Asynchronous Inference endpoint. Configure an auto scaling policy that scales in to zero outside business hours.
  • DDeploy the model to a SageMaker real-time endpoint. Create a scheduled AWS Lambda function that activates the endpoint during business hours only.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Deploy the model to a SageMaker Serverless Inference endpoint. Configure increased provisioned concurrency during business hours.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance. This option delivers the requirement at the lowest cost.

MLA-C01 Question 130

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Topic 1 · Question 130 · Select all that apply

An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)

  • AUse Amazon Rekognition to automatically label the dataset.
  • BTrain the deep learning model directly on the raw data. Let the model infer the labels by itself.
  • CUse Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements. (correct answer)
  • DSet up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth. (correct answer)
  • EUse Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.
Reveal answer & explanation
Correct answer: C, D

The correct answer is C, D. Option C: Use Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements. Option D: Set up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.

Explanation

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

MLA-C01 Question 131

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Topic 1 · Question 131

A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data. Which solution will meet these requirements?

  • ACreate a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.
  • BCreate a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job. (correct answer)
  • CCreate a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to the S3 bucket. Clean and normalize the data by using a DataBrew profile job.
  • DCreate a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to the S3 bucket. Clean and normalize the data by using a DataBrew recipe job.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed.

MLA-C01 Question 132

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Topic 1 · Question 132

A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format. An ML engineer needs to convert the JSON files into a tabular format. Which solution will meet this requirement with the LEAST operational overhead?

  • ACreate an AWS Glue PySpark job that uses the Relationalize transform to convert the files. (correct answer)
  • BWrite custom Scala code to convert the files. Use Amazon EMR Serverless to run the Scala code.
  • CCreate an AWS Lambda function that uses a Python runtime and invokes the reduce() function to convert the files. Invoke the Lambda function.
  • DCreate an Amazon Athena database that is based on the JSON files. Use the Athena flatten function to convert the data.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Create an AWS Glue PySpark job that uses the Relationalize transform to convert the files.

Explanation

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

MLA-C01 Question 133

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Topic 1 · Question 133

A medical company ingests streams of data from devices that monitor patients’ vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features. An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration. Which solution will meet these requirements with the LEAST implementation effort?

  • AUse Amazon CloudWatch to create custom metrics for the characteristics of each iteration.
  • BWrite the characteristics of each iteration to logs in Amazon S3. Use AWS Glue and Amazon Athena to search the logs.
  • CUse the SageMaker Model Registry to track the characteristics and results of each iteration.
  • DUse SageMaker Experiments to track the characteristics and results of each iteration. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use SageMaker Experiments to track the characteristics and results of each iteration.

Explanation

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

MLA-C01 Question 134

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Topic 1 · Question 134 · Select all that apply

A company is planning to create an internal-only chat interface to help employees handle customer queries. Currently, the employees need to refer to a massive knowledge base of internal documents to address customer issues. The new solution must be serverless. Which combination of steps will meet these requirements?

  • ASet up Amazon Bedrock with the Anthropic Claude foundation model. (correct answer)
  • BSet up Amazon SageMaker JumpStart with the Llama foundation model.
  • CUse Amazon EC2 instances with Amazon API Gateway to invoke the model API.
  • DUse AWS Lambda functions with Amazon API Gateway to invoke the model API. (correct answer)
  • EUse an Amazon S3 bucket to store vector database dumps and embeddings. (correct answer)
  • FUse Amazon RDS for MySQL to store vector database dumps and embeddings.
Reveal answer & explanation
Correct answer: A, D, E

The correct answer is A, D, E. Option A: Set up Amazon Bedrock with the Anthropic Claude foundation model. Option D: Use AWS Lambda functions with Amazon API Gateway to invoke the model API. Option E: Use an Amazon S3 bucket to store vector database dumps and embeddings.

Explanation

AWS Lambda runs code without provisioning servers and scales automatically, which minimizes operational overhead. Amazon S3 provides durable, scalable object storage that is fully managed. Amazon API Gateway is a fully managed front door for creating and securing APIs at scale.

MLA-C01 Question 135

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Topic 1 · Question 135

An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions. When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data. Which deployment solution will meet these requirements with the LEAST operational overhead?

  • ADeploy the model to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
  • BDeploy the model to an Amazon SageMaker real-time endpoint.
  • CDeploy the model to an Amazon SageMaker Asynchronous Inference endpoint. (correct answer)
  • DPackage the model as a container. Deploy the model to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2 instances.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Deploy the model to an Amazon SageMaker Asynchronous Inference endpoint.

Explanation

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

MLA-C01 Question 136

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Topic 1 · Question 136 · Select all that apply

An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either “yes” or “no.” The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset. Which methods will meet these requirements? (Choose two.)

  • ABinary encoding (correct answer)
  • BLabel encoding (correct answer)
  • COne-hot encoding
  • DStatistical imputation
  • ETokenization
Reveal answer & explanation
Correct answer: A, B

The correct answer is A, B. Option A: Binary encoding Option B: Label encoding.

MLA-C01 Question 138

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Topic 1 · Question 138

A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data. Which SageMaker algorithm should the company choose for the recommendation model?

  • AK-nearest neighbors (k-NN)
  • BFactorization Machines (correct answer)
  • CPrincipal component analysis (PCA)
  • DSequence-to-Sequence (seq2seq)
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Factorization Machines.

MLA-C01 Question 139

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Topic 1 · Question 139

A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks. The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry. Which solution will meet these requirements with the LEAST operational overhead?

  • AExport the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
  • BImport the models into the SageMaker Model Registry. Use SageMaker to run the imported models.
  • CUse code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
  • DImport the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.

Explanation

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

MLA-C01 Question 140

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Topic 1 · Question 140

A company is training a large language model (LLM) by using on-premises infrastructure. A live conversational engine uses the LLM to help customers find real-time insights in credit card data. An ML engineer must implement a solution to train and deploy the LLM on Amazon SageMaker. Which solution will meet these requirements?

  • AUse SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real-time inference.
  • BUse SageMaker with deep learning containers for large model inference to train the LLM. Deploy the LLM by using SageMaker real-time inference. (correct answer)
  • CUse SageMaker Notebook Jobs to train the LLM. Deploy the LLM by using SageMaker Asynchronous Inference.
  • DUse SageMaker Studio to train the LLM. Deploy the LLM by using SageMaker batch transform.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Use SageMaker with deep learning containers for large model inference to train the LLM. Deploy the LLM by using SageMaker real-time inference.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option meets the real-time / low-latency performance requirement.

MLA-C01 Question 141

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Topic 1 · Question 141

A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1. The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2. Which solution will meet these requirements?

  • ACreate a second production variant for v2. Assign 1% of the traffic to v2 and 99% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
  • BCreate a second production variant for v2. Assign 10% of the traffic to v2 and 90% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
  • CDeploy v2 to a new endpoint. Turn on data capturing for the production endpoint. Write a script to pass 100% of input data to v2. If v2 performs as expected, deactivate the v1 endpoint and direct the traffic to v2.
  • DDeploy v2 into a shadow variant that samples 100% of the inference requests. Collect all the output in an Amazon S3 bucket. If v2 performs as expected, promote v2 to production. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Deploy v2 into a shadow variant that samples 100% of the inference requests. Collect all the output in an Amazon S3 bucket. If v2 performs as expected, promote v2 to production.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed.

Showing questions 121–140 of 226 · Page 7 of 12