MLA-C01 practice questions 121–130

1. Question 121 · 226 in bank

   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.

   Show answer

   Answer: D

   Next question

2. Question 122 · 226 in bank

   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.

   Show answer

   Answer: D

   Next question

3. Question 123 · 226 in bank

   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.
   • 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.

   Show answer

   Answer: A

   Next question

4. Question 124 · 226 in bank

   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.
   • CConfigure a serverless inference endpoint.
   • DConfigure an asynchronous inference endpoint.

   Show answer

   Answer: B

   Next question

5. Question 125 · 226 in bank

   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.
   • DUse the Feature Store Spark connector to ingest the data as Spark DataFrames with only the online store enabled.

   Show answer

   Answer: C

   Next question

6. Question 126 · 226 in bank

   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.
   • CCreate multiple SageMaker single-model endpoints.
   • DRun a SparkML job to generate multiple endpoints.

   Show answer

   Answer: B

   Next question

7. Question 127 · 226 in bank

   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.
   • 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.

   Show answer

   Answer: A

   Next question

8. Question 128 · 226 in bank

   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.
   • CCreate and use Amazon Mechanical Turk entities in an Amazon SageMaker human loop.
   • DUse the Amazon Mechanical Turk website.

   Show answer

   Answer: B

   Next question

9. Question 129 · 226 in bank

   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.
   • 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.

   Show answer

   Answer: B

   Next question

10. Question 130 · 226 in bank

    Select 2

    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.
    • DSet up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.
    • EUse Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.

    Show answer

    Answer: CD

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

MLA-C01 practice, question 121. 226 questions available.