MLS-C01 Data Engineering Practice Test 3 — 15 Questions

Question 1

Drag and drop the steps to set up cross-validation in a SageMaker training job using the built-in XGBoost algorithm in the correct order.

Question 2

Match each AWS service to its primary purpose in a machine learning pipeline.

Question 3

Match each AWS security service to its function in ML.

Question 4

Match each data format to its typical use in AWS ML.

Question 5

Match each SageMaker optimization technique to its description.

Question 6

A data engineering team needs to process streaming data from thousands of IoT devices. They want to aggregate data in 1-minute windows and store results in an S3 data lake for downstream analytics. Which architecture should they use?

Accepted Answer

Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.. Option D is correct because Kinesis Data Analytics provides real-time SQL-based processing with windowing functions, and Kinesis Firehose can deliver aggregated data directly to S3. Option A is wrong because Lambda scales but has a 15-minute timeout and is not ideal for heavy streaming aggregation. Option B is wrong because Kinesis Data Streams alone does not process data; it requires a consumer. Option C is wrong because Glue is batch-oriented, not real-time.

Answer

Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.

Answer

Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.

Answer

Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.

Question 7

A company uses AWS Glue ETL jobs to transform CSV data from an S3 bucket into Parquet. The jobs often fail with memory errors when processing large datasets. They want to minimize cost and improve reliability. What should they do?

Accepted Answer

Use G.1X or G.2X worker types and increase the number of DPUs per worker.. Option B is correct because increasing the number of DPUs per worker and using G.1X workers provides more memory and compute per task, reducing out-of-memory errors. Option A is wrong because Spark's shuffle behavior can still cause memory issues even with more workers. Option C is wrong because S3 batch operations are not suitable for complex transformations. Option D is wrong because Presto is interactive, not designed for scheduled ETL.

Answer

Use Amazon Athena with CTAS queries to convert the data to Parquet.

Answer

Switch to S3 Batch Operations with AWS Lambda to process the files individually.

Answer

Increase the number of workers in the Glue job configuration.

Question 8

A machine learning team needs to create a training dataset by joining two large datasets (10 TB and 5 TB) stored in S3. The join key is 'user_id'. They want to minimize data movement and cost. Which approach should they use?

Accepted Answer

Use Amazon Athena to run a SQL query joining the two datasets directly on S3.. Option C is correct because Amazon Athena allows serverless SQL joins directly on S3 data without moving it, and is cost-effective for large ad-hoc queries. Option A is wrong because Redshift Spectrum still requires moving data into Redshift for optimal performance. Option B is wrong because EMR requires provisioning clusters and incurs compute costs even when idle. Option D is wrong because Glue ETL typically moves data into a transformation environment.

Answer

Use AWS Glue ETL to read both datasets, join them using Spark DataFrames, and write the result to S3.

Answer

Launch an Amazon EMR cluster with Spark, read data from S3, perform the join, and write results back to S3.

Answer

Load both datasets into Amazon Redshift using COPY commands, then perform the join in Redshift.

Question 9

A company uses an Amazon SageMaker notebook to train a model using data from an S3 bucket. The IAM role attached to the notebook has the following policy. What is the MOST specific change needed to allow the notebook to read from the bucket 'ml-data-123'?

Accepted Answer

Remove the Deny statement from the IAM policy.. Option A is correct because the existing policy denies access to the specific bucket, so the explicit deny must be removed. Option B is wrong because the AllowedPrincipal is not required for S3 bucket policies. Option C is wrong because S3 access points are not necessary. Option D is wrong because an explicit allow cannot override an explicit deny.

Answer

Add an Allow statement for 's3:GetObject' on 'ml-data-123' to the IAM policy.

Answer

Create an S3 access point and update the IAM policy to use the access point ARN.

Answer

Add a bucket policy on 'ml-data-123' that grants access to the notebook's IAM role.

Question 10

A data engineer needs to transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?

Accepted Answer

Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.. Option B is correct because AWS Glue crawlers can catalog the data, and Glue ETL jobs are serverless, cost-effective, and can be scheduled. Option A is wrong because Athena is for querying, not ETL. Option C is wrong because EMR requires cluster management. Option D is wrong because Lambda has execution time limits and is not ideal for large datasets.

Answer

Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.

Answer

Use AWS Lambda functions triggered by S3 events to transform each file incrementally.

Answer

Use Amazon Athena to run CTAS queries to convert and partition the data daily.

Question 11

A company uses Kinesis Data Streams to ingest real-time sensor data. The data is consumed by a Lambda function that writes to DynamoDB. During peak hours, the Lambda function throws ProvisionedThroughputExceededException. The team wants to decouple the write operation and improve resilience. What should they do?

Accepted Answer

Use Kinesis Firehose as a consumer of the stream, with a Lambda transformation to write to DynamoDB, and enable error handling.. Option A is correct because Kinesis Firehose can buffer data and write to DynamoDB via Lambda, providing retry and decoupling. Option B is wrong because SQS does not integrate directly with Kinesis. Option C is wrong because Kinesis Data Analytics does not write to DynamoDB directly. Option D is wrong because Lambda is already being used; the issue is throughput, not compute.

Answer

Increase the Lambda function's reserved concurrency and provision more DynamoDB write capacity.

Answer

Place the Lambda function's output into an Amazon SQS queue, and have a second Lambda function write to DynamoDB.

Answer

Use Kinesis Data Analytics to process the stream and write results directly to DynamoDB.

Question 12

A data engineer needs to design a data pipeline that ingests CSV files from an SFTP server daily, transforms them, and loads them into Amazon Redshift. The files are typically 2-3 GB. Which combination of AWS services is MOST appropriate?

Accepted Answer

Use AWS Transfer Family to automate SFTP file retrieval to S3, then use Redshift COPY to load data.. Option D is correct because AWS Transfer Family provides SFTP integration, and Amazon Redshift COPY command efficiently loads large files from S3. Option A is wrong because Lambda has a 15-minute timeout and 6 MB payload limit for invocation. Option B is wrong because Glue ETL can read from S3 but does not directly connect to SFTP. Option C is wrong because Kinesis is for streaming, not batch file transfers.

Answer

Use AWS Glue ETL with a JDBC connection to the SFTP server to read files directly.

Answer

Use AWS Lambda to download the files from SFTP, transform them in memory, and write to Redshift using the Data API.

Answer

Use Amazon Kinesis Data Firehose with an HTTP endpoint source to receive files from SFTP.

Question 13

A team stores raw data in S3 and uses a Glue Data Catalog for metadata. They want to allow data scientists to query the data with Amazon Athena using their existing IAM roles. What is the MINIMUM set of permissions required?

Accepted Answer

Grant the IAM role permissions for Athena actions, Glue Data Catalog actions, and S3 read access.. Option C is correct because Athena needs permissions to query the Glue Data Catalog and to read data from S3. Option A is wrong because write permissions to S3 are not needed for querying. Option B is wrong because Glue job execution is separate. Option D is wrong because Kinesis permissions are irrelevant.

Answer

Grant the IAM role permissions for Athena, Glue, and S3 (read and write).

Answer

Grant the IAM role permissions for Athena and Amazon Redshift Spectrum.

Answer

Grant the IAM role permissions for Athena and Amazon Kinesis.

Question 14

A company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink.. Option B is correct because Kinesis Data Analytics is designed for real-time streaming analytics with sub-second latency and is fully managed. Option A is wrong because Redshift is not designed for sub-second streaming ingestion; it is batch-oriented. Option C is wrong because EMR requires cluster management. Option D is wrong because Lambda has concurrency limits and is not optimized for millions of events per second.

Answer

AWS Lambda functions triggered by Kinesis Data Streams.

Answer

Amazon EMR with Spark Streaming.

Answer

Amazon Redshift with materialized views refreshed frequently.

Question 15

A data engineer needs to set up a data lake on S3 that supports both batch and streaming ingestion. The data must be queryable by Athena, Redshift Spectrum, and EMR. Which TWO configurations are essential? (Choose two.)

Accepted Answer

Store data in columnar formats like Parquet or ORC.. Options A and B are correct. Option A ensures the Glue Data Catalog is used as a metastore for all services. Option B stores data in columnar format for query efficiency. Option C is wrong because S3 Select is not required. Option D is wrong because Kinesis is needed only for streaming. Option E is wrong because S3 versioning is not essential for querying.

Answer

Enable S3 Select on the target buckets.

Answer

Enable S3 versioning on all buckets.

Answer

Set up Kinesis Data Firehose for streaming ingestion.