Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 61:

  A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required. What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

  A. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.
  B. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
  C. Create a SageMaker processing job by calling the SageMaker Python SDK.
  D. Create a data flow in SageMaker Data Wrangler. Configure a transform step.
  D. Create a data flow in SageMaker Data Wrangler. Configure a transform step.

• Question 62:

  An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

  A. AWS::SageMaker::Model
  B. AWS::SageMaker::Endpoint
  C. AWS::SageMaker::NotebookInstance
  D. AWS::SageMaker::Pipeline
  A. AWS::SageMaker::Model

• Question 63:

  A company plans to implement Retrieval Augmented Generation (RAG) to enhance an open-source large language model (LLM) running on Amazon Bedrock. The data for RAG consists of .csv and .docx files stored in an Amazon S3 bucket. The solution must minimize operational overhead.

  Which approach would best satisfy these requirements with the least operational complexity?

  A. Create a pipeline in Amazon SageMaker Pipelines to generate a new model. Call the new model from Amazon Bedrock to perform RAG queries.
  B. Convert the data into vectors. Store the data in an Amazon Neptune database. Connect the database to Amazon Bedrock. Call the Amazon Bedrock API to perform RAG queries.
  C. Fine-tune an existing LLM by using an AutoML job in Amazon SageMaker. Configure the S3 bucket as a data source for the AutoML job. Deploy the LLM to a SageMaker endpoint. Use the endpoint to perform RAG queries.
  D. Create a knowledge base for Amazon Bedrock. Configure a data source that references the S3 bucket. Use the Amazon Bedrock API to perform RAG queries.
  D. Create a knowledge base for Amazon Bedrock. Configure a data source that references the S3 bucket. Use the Amazon Bedrock API to perform RAG queries.

• Question 64:

  HOTSPOT

  A company wants to host an ML model on Amazon SageMaker. An ML engineer is con guring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.

  Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)

  1. An S3 event notfication invokes the pipeline when new data is uploaded.

  2. S3 Lifecycle rule invokes the pipeline when new data is uploaded.

  3. SageMaker retrains the model by using the data in the S3 bucket.

  4. The pipeline deploys the model to a SageMaker endpoint.

  5. The pipeline deploys the model to SageMaker Model Registry.

  

  

• Question 65:

  A company has a team of data scientists who use Amazon SageMaker 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 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.
  A. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.

• Question 66:

  HOTSPOT

  A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models. Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)

  1. Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.

  2. Store the resulting data back in Amazon S3.

  3. Use Amazon Athena to infer the schemas and available columns.

  4. Use AWS Glue crawlers to infer the schemas and available columns.

  5. Use AWS Glue DataBrew for data cleaning and feature engineering.

  

  

• Question 67:

  A company is developing a real-time data processing pipeline for an e-commerce application that generates large volumes of clickstream data. The data needs to be ingested, processed, and visualized in near real-time. Additionally, the solution must support SQL for data processing and Jupyter notebooks for interactive analysis.

  Which solution would best meet these requirements?

  A. Use 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.
  B. Use 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.
  C. Use 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.
  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.
  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.

• Question 68:

  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.
  D. Run AWS Compute Optimizer.

• Question 69:

  A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream

  is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard. The company needs to optimize the data ingestion pipeline to support sub-

  second latency for the real-time dashboard.

  Which change to the architecture will meet these requirements?

  A. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
  B. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
  C. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
  D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
  A. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.

• Question 70:

  An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The

  values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.

  What should the ML engineer do to improve the training process?

  A. Introduce early stopping.
  B. Increase the size of the test set.
  C. Increase the learning rate.
  D. Decrease the learning rate.
  D. Decrease the learning rate.