A retail company has customer data in an Amazon RDS for PostgreSQL database and purchase history in Amazon S3 as Parquet files. The ML team needs to create a unified dataset for training a recommendation model. The data volumes are moderate (tens of GB). The team wants to use a serverless approach to merge the data and write the result back to S3. Which solution should they use?

Use AWS Lambda to read from RDS and S3, join the data in memory, and write to S3.

Use Amazon Athena to query both RDS and S3 directly and create a new table in S3.

Use Amazon EMR with a Spark application running on a persistent cluster.

Use AWS Glue with a Python (Spark) script to read from RDS and S3, perform the join, and write the output to S3.

When a Kinesis Data Stream is receiving more data than the shards can handle, which action is the most direct way to increase the stream's capacity?

Use a larger instance type for the producer

Increase the retention period of the stream

Enable enhanced fan-out on the stream

Increase the number of shards in the stream

A research team needs to transfer a 2 TB dataset from an on-premises data center to Amazon S3 for ML training. The dataset consists of millions of small files (average 100 KB). The team has a 1 Gbps dedicated network connection to AWS and needs to complete the transfer within 24 hours. Which approach will most efficiently meet the requirement?

Use AWS Snowball Edge to physically ship the data.

Use AWS DataSync to transfer the data over the dedicated connection.

Use the AWS CLI with the s3 cp command to copy the files.

Use S3 Transfer Acceleration to speed up the transfer over the public internet.

AWSCertified Machine Learning Engineer - Associate

Domain 1Objective 1

Task 1.1: Ingest and Store Data MLA-C01 Practice Questions (Page 4)

Part of the Content Domain 1: Data Preparation for Machine Learning (ML) domain, which accounts for 28% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~14–24 in this domain), expect 5–8 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

10concepts

28%of the exam

Questions 16–20

16foundation · easy
Which data format is the best choice for analytical queries that frequently filter on specific columns, because it stores data in a columnar layout that reduces I/O and improves compression?
Select an answer first
17application · medium
A retail company has customer data in an Amazon RDS for PostgreSQL database and purchase history in Amazon S3 as Parquet files. The ML team needs to create a unified dataset for training a recommendation model. The data volumes are moderate (tens of GB). The team wants to use a serverless approach to merge the data and write the result back to S3. Which solution should they use?
Select an answer first
18foundation · easy
When a Kinesis Data Stream is receiving more data than the shards can handle, which action is the most direct way to increase the stream's capacity?
Select an answer first
19application · medium
A research team needs to transfer a 2 TB dataset from an on-premises data center to Amazon S3 for ML training. The dataset consists of millions of small files (average 100 KB). The team has a 1 Gbps dedicated network connection to AWS and needs to complete the transfer within 24 hours. Which approach will most efficiently meet the requirement?
Select an answer first
20foundation · easy
Which open-source distributed computing framework is commonly used to process and merge large datasets across multiple sources, and is available as a managed service on AWS through Amazon EMR?
Select an answer first

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Task 1.1: Ingest and Store Data MLA-C01 Practice Questions (Page 4)

Questions 16–20

Which data format is the best choice for analytical queries that frequently filter on specific columns, because it stores data in a columnar layout that reduces I/O and improves compression?

When a Kinesis Data Stream is receiving more data than the shards can handle, which action is the most direct way to increase the stream's capacity?

Which open-source distributed computing framework is commonly used to process and merge large datasets across multiple sources, and is available as a managed service on AWS through Amazon EMR?

Keep studying — Content Domain 1: Data Preparation for Machine Learning (ML)