AWS Certified Machine Learning Engineer - Associate MLA-C01 practice test

1. Question 1 · 1

   Case Study - 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 needs to use the central model registry to manage different versions of models in the application. Which action will meet this requirement with the LEAST operational overhead?

   Choose one answer.

   • Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
   • Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
   • Use the SageMaker Model Registry and model groups to catalog the models.
   • Use the SageMaker Model Registry and unique tags for each model version.

2. Question 2 · 1

   Case study - An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. Which AWS service or feature can aggregate the data from the various data sources?

   Choose one answer.

   • Amazon EMR Spark jobs
   • Amazon Kinesis Data Streams
   • Amazon DynamoDB
   • AWS Lake Formation

3. Question 3 · 1

   A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet. Which solution will meet this requirement?

   Choose one answer.

   • Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
   • Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.
   • 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).
   • 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).

4. Question 4 · 1

   A company needs an AWS solution that will automatically create versions of ML models as the models are created. Which solution will meet this requirement?

   Choose one answer.

   • Amazon Elastic Container Registry (Amazon ECR)
   • Model packages from Amazon SageMaker Marketplace
   • Amazon SageMaker ML Lineage Tracking
   • Amazon SageMaker Model Registry

5. Question 5 · 1

   A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files. Which solution will meet these requirements with the LEAST operational overhead?

   Choose one answer.

   • Create a pipeline in Amazon SageMaker Pipelines to generate a new model. Call the new model from Amazon Bedrock to perform RAG queries.
   • 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.
   • 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.
   • 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.

6. Question 6 · 1

   A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less. Which SageMaker inference option will meet these requirements?

   Choose one answer.

   • Serverless inference
   • Asynchronous inference
   • Real-time inference
   • Batch transform

7. Question 7 · 1

   An ML engineer notices class imbalance in an image classification training job. What should the ML engineer do to resolve this issue?

   Choose one answer.

   • Reduce the size of the dataset.
   • Transform some of the images in the dataset.
   • Apply random oversampling on the dataset.
   • Apply random data splitting on the dataset.

8. Question 8 · 1

   A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to implement a solution to mask credit card numbers in the files before the model is retrained. Which solution will meet this requirement with the LEAST development effort?

   Choose one answer.

   • Create a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
   • Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.
   • Create Apache Spark code to run on an AWS Glue job. Program the code to perform a regex operation to find and mask sensitive data.
   • Create Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.

9. Question 9 · 1

   A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from patients. The company needs to use this information to gain insight about the patients. Which solution will meet this requirement with the LEAST development effort?

   Choose one answer.

   • Use Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.
   • Use Amazon Comprehend Medical to summarize the data.
   • Use Amazon Kendra to create a quick-search tool to query the data.
   • Use the Amazon SageMaker Sequence-to-Sequence (seq2seq) algorithm to create a text summary from the data.

10. Question 10 · 1

    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?

    Choose one answer.

    • Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.
    • Use an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
    • Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.
    • Use Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.

11. Question 11 · 1

    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?

    Choose one answer.

    • Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.
    • Set up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
    • Set up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.
    • Set up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.

12. Question 12 · 1

    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?

    Choose one answer.

    • Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.
    • Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.
    • Create 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.
    • Create 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.

13. Question 13 · 1

    Case study - An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result. Which solution will meet these requirements?

    Choose one answer.

    • Use Amazon Athena to automatically detect the anomalies and to visualize the result.
    • Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
    • Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
    • Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

14. Question 14 · 1

    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?

    Choose one answer.

    • Schedule a SageMaker Model Monitor job. Observe metrics about model quality.
    • Schedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
    • Enable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
    • Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.

15. Question 15 · 1

    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?

    Choose one answer.

    • 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.
    • Use 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.
    • Use 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.
    • Use 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.

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