Free AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question - Page 5

Question 13 (Topic 1)

A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet. An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data. The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future. Which solution will meet these requirements?

Select an option, then click Submit answer.

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

Reference / correct answer:

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.

Most accepted answer: 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.

Community votes: A=3, C=5

Selected Answer: C ✅ Explanation: Problem Summary: The data in S3 is mixed file formats: CSV, JSON, XLSX, and Parquet — all in one folder. You need to use AWS Glue DataBrew to process the data. The processed data must be stored in S3 for AWS Glue to consume later. Key Considerations: DataBrew Input Requirements: DataBrew datasets must be in a consistent format (CSV, JSON, XLSX, or Parquet). DataBrew cannot process mixed formats in a single dataset. You must split the data by format. DataBrew Output Format: Apache Parquet is preferred for: Efficient storage Better performance with AWS Glue and other analytics tools Columnar storage benefits in querying and transformations "AWS Glue Parquet format" does not exist — this is a distractor in the answer options. upvoted 6 times

Selected Answer: C DataBrew requires s3 buckets contains same format of data so it is c upvoted 1 times

Selected Answer: C Confusing, but I'd go with C. upvoted 1 times

Selected Answer: C AWS Glue DataBrew works best when processing files of the same type together. It's designed to apply consistent transformations to datasets with similar structures. When working with multiple file formats (CSV, JSON, XLSX, and Parquet), separating them by type allows for format-specific preprocessing before merging the data. upvoted 3 times

Selected Answer: A 🔑 Keyword: Process mixed file types with AWS Glue DataBrew & store for AWS Glue ✅ Correct Answer: A. Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format. Why? AWS Glue performs best with Parquet because it is optimized for analytical queries. No need to split data into separate folders—DataBrew can handle mixed file types. Why Others Are Wrong? ❌ B. "AWS Glue Parquet format" is not a valid term. Apache Parquet is the correct format. ❌ C & D. Separating files into different folders is unnecessary—DataBrew can process multiple formats in a single folder. upvoted 2 times

Question 14 (Topic 1)

An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Encrypt communication between nodes for batch processing.
○ Encrypt communication between nodes in a training cluster.
○ Specify an AWS Key Management Service (AWS KMS) key during creation of the training job request.
○ Specify an AWS Key Management Service (AWS KMS) key during creation of the SageMaker domain.

Question 15 (Topic 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?

Select an option, then click Submit 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.

Reference / correct answer:

Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.

Most accepted answer: A. Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.

Community votes: A=6, C=3

Selected Answer: A https://aws.amazon.com/blogs/aws/now-process-pdfs-word-documents-and-images-with-amazon-comprehend-for-idp/ Amazon Comprehend feature for intelligent document processing (IDP). This feature allows you to classify and extract entities from PDF documents, Microsoft Word files, and images directly from Amazon Comprehend without you needing to extract the text first. upvoted 6 times

Selected Answer: A Amazon Comprehend can extract directly from PDF. upvoted 5 times

Selected Answer: A C is incorrect as Comprehend extracts entities FROM text, not "converts entities to text" upvoted 1 times

Selected Answer: A Comprehend strengths: Fully managed nlp service built-in entity extraction capability no training required immediate processing direct integration with S3 Can detect entities like: names, dates, organizations, locations, and custom entities upvoted 1 times

Selected Answer: C Correct answer is C. Comprehend can't directly extract from PDF as it extracts from texts. So, you need to use Textract to extract from PDF first and then use Comprehend. upvoted 2 times

Free AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 – Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question