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MLA-C01 Real Exam Questions

AWS Certified Machine Learning Engineer - Associate (MLA-C01)

124 questions available · Page 1 of 13

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Question 1 Single choice

A company is running ML models on premises by using custom Python scripts and proprietary datasets.
The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.

Which solution will meet these requirements with the LEAST effort?

  1. A

    Use SageMaker built-in algorithms to train the proprietary datasets.

  2. B

    Use SageMaker script mode and premade images for ML frameworks.

  3. C

    Build a container on AWS that includes custom packages and a choice of ML frameworks.

  4. D

    Purchase similar production models through AWS Marketplace.

Show answer and explanation

Correct answer: B

Question 2 Single choice

An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes

processing time for the data.

Which file format will meet these requirements?

  1. A

    CSV files compressed with Snappy

  2. B

    JSON objects in JSONL format

  3. C

    JSON files compressed with gzip

  4. D

    Apache Parquet files

Show answer and explanation

Correct answer: D

Question 3 Single choice

A company has an Amazon S3 bucket containing a large volume of files from various sources, all stored within the same S3 folder. The files include different formats such as CSV, JSON, XLSX, and Apache Parquet.

An ML engineer is tasked with implementing a solution that leverages AWS Glue DataBrew to process this data. Additionally, the engineer must ensure that the final output is stored in Amazon S3, enabling AWS Glue to access and consume the data in the future.

Which solution will effectively satisfy these requirements?

  1. A

    Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.

  2. B

    Use DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.

  3. C

    Separate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in Apache Parquet format.

  4. D

    Separate the data into a different folder for each file type. Use DataBrew to process each folder

    individually. Store the output in AWS Glue Parquet format.

Show answer and explanation

Correct answer: C

Question 4 Single choice

A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.

Which solution will meet these requirements?

  1. A

    Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.

  2. B

    Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.

  3. C

    Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.

  4. D

    Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query

    Amazon Textract to perform the semantic searches.

Show answer and explanation

Correct answer: C

Question 5 Single choice

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. The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.

Which algorithm should the ML engineer use to meet this requirement?

  1. A

    LightGBM

  2. B

    Linear learner

  3. C

    means clustering

  4. D

    Neural Topic Model (NTM)

Show answer and explanation

Correct answer: B

Question 6 Single choice

A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is Configured properly, and ML engineers can access the SageMaker domain. Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.

Which update to the network con guration will meet this requirement?

  1. A

    Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.

  2. B

    Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.

  3. C

    Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.

  4. D

    Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

Show answer and explanation

Correct answer: B

Question 7 Single choice

An ML engineer must ensure that all data is encrypted in transit during the execution of an ML training job.
This includes applying encryption in transit to all processes utilized by Amazon SageMaker throughout the training job.

Which solution will effectively fulfill these requirements?

  1. A

    Encrypt communication between nodes for batch processing.

  2. B

    Encrypt communication between nodes in a training cluster.

  3. C

    Specify an AWS Key Management Service (AWS KMS) key during creation of the training job request.

  4. D

    Specify an AWS Key Management Service (AWS KMS) key during creation of the SageMaker domain.

Show answer and explanation

Correct answer: B

Question 8 Single choice

A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.

Which technique for feature engineering should the ML engineer use for the model?

  1. A

    Apply label encoding to the color categories. Automatically assign each color a unique integer.

  2. B

    Implement padding to ensure that all color feature vectors have the same length.

  3. C

    Perform dimensionality reduction on the color categories.

  4. D

    One-hot encode the color categories to transform the color scheme feature into a binary matrix.

Show answer and explanation

Correct answer: D

Question 9 Single choice

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?

  1. A

    Set up SageMaker Debugger and create a custom rule.

  2. B

    Set up blue/green deployments with all-at-once traffic shifting.

  3. C

    Set up blue/green deployments with canary traffic shifting.

  4. D

    Set up shadow testing with a shadow variant of the new model.

Show answer and explanation

Correct answer: D

Question 10 Single choice

A company is planning to use Amazon SageMaker to make classication ratings that are based on images.
The company has 6 of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.

An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.

Which solution will meet these requirements?

  1. A

    Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.

  2. B

    Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.

  3. C

    Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

  4. D

    Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

Show answer and explanation

Correct answer: A