Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 21:

  A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score. During a baseline

  analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases signi cantly.

  What could be the reason for the reduced F1 score?

  A. Concept drift occurred in the underlying customer data that was used for predictions.
  B. The model was not su ciently complex to capture all the patterns in the original baseline data.
  C. The original baseline data had a data quality issue of missing values.
  D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.
  A. Concept drift occurred in the underlying customer data that was used for predictions.

• Question 22:

  A company is using Amazon SageMaker to create ML models. The company's data scientists need ne-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and

  workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.

  Which solution will meet these requirements?

  A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
  B. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
  C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
  D. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
  C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

• Question 23:

  A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model

  monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company must implement a manual approval-based work ow to ensure that only

  approved models can be deployed to production endpoints.

  Which solution will meet this requirement?

  A. Use SageMaker Experiments to facilitate the approval process during model registration.
  B. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  C. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
  D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

• Question 24:

  A nancial 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 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 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.
  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.

• Question 25:

  A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 in size and consists of CSV, JSON, Apache Parquet, and simple textfiles. The data must be

  processed in several consecutive steps. The steps include complex manipulations that can take hours to nish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be

  automated.

  Which solution will meet these requirements?

  A. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  B. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  C. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
  D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.

• Question 26:

  A company needs to perform real-time predictions using a pre-trained model. Which service should they use?

  A. AWS Batch
  B. Amazon Kinesis Data Streams
  C. Amazon SageMaker Endpoints
  D. AWS Glue
  C. Amazon SageMaker Endpoints

• Question 27:

  A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months. Which solution will meet this requirement?

  A. Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.
  B. Store the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
  C. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.
  D. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.
  A. Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.

• Question 28:

  A company is using Amazon SageMaker to develop machine learning models and stores sensitive training data in an Amazon S3 bucket. To comply with security requirements, the model training process must be isolated from the internet. Which solution would effectively ensure network isolation during model training?

  A. Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
  B. Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.
  C. Run the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
  D. Encrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
  B. Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.

• Question 29:

  A company is developing an ML project that involves using Amazon SageMaker notebook instances. An ML engineer must ensure that these notebook instances are configured to prevent root access. Which solution would effectively restrict the deployment of notebook instances with root access?

  A. Use IAM condition keys to stop deployments of SageMaker notebook instances that allow root access.
  B. Use AWS Key Management Service (AWS KMS) keys to stop deployments of SageMaker notebook instances that allow root access.
  C. Monitor resource creation by using Amazon EventBridge events. Create an AWS Lambda function that deletes all deployed SageMaker notebook instances that allow root access.
  D. Monitor resource creation by using AWS CloudFormation events. Create an AWS Lambda function that deletes all deployed SageMaker notebook instances that allow root access.
  A. Use IAM condition keys to stop deployments of SageMaker notebook instances that allow root access.

• Question 30:

  A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and

  without affecting production end users.

  Which solution will meet these requirements?

  A. Set up SageMaker Debugger and create a custom rule.
  B. Set up blue/green deployments with all-at-once traffic shifting.
  C. Set up blue/green deployments with canary traffic shifting.
  D. Set up shadow testing with a shadow variant of the new model.
  D. Set up shadow testing with a shadow variant of the new model.