MLS-C01 Data Engineering Practice Test 11 — 15 Questions

Question 1

A company is using Amazon SageMaker to train a model on a dataset that is updated daily. The data is stored in an S3 bucket. The training pipeline uses AWS Step Functions to orchestrate data preprocessing and model training. The preprocessing step uses a SageMaker Processing job that reads data from S3, cleans it, and writes the output back to S3. The team notices that the training step often fails due to insufficient disk space on the processing instance. Which change should the team make to resolve this issue without increasing cost?

Accepted Answer

Configure the processing job to use local instance store (SSD) for scratch space.. Option B is correct because using local instance store provides more disk space at no extra cost compared to EBS. Option A is wrong because using a different instance type may increase cost. Option C is wrong because using S3 as intermediate storage is not the issue. Option D is wrong because the team already uses SageMaker Processing, which is appropriate.

Answer

Enable automatic scaling for the processing job.

Answer

Use AWS Batch instead of SageMaker Processing.

Answer

Use a larger instance type with more memory.

Question 2

A team is building a data pipeline that ingests data from an Amazon S3 bucket, transforms it using AWS Glue, and loads it into Amazon Redshift for analysis. The Glue job runs on a schedule every hour. The team has noticed that the job takes longer than expected and sometimes fails due to memory issues. The data volume is variable, with occasional spikes. Which solution should the team implement to optimize the pipeline?

Accepted Answer

Enable job bookmarks to process only new data and use a G.2X worker type for more memory.. Option C is correct because Glue Job Bookmarks help track processed data and avoid reprocessing, and using a larger worker type with more memory can handle spikes. Option A is wrong because increasing the schedule frequency would not address the root cause. Option B is wrong because using a smaller worker type would worsen memory issues. Option D is wrong because Spark is already used internally.

Answer

Decrease the number of workers to reduce memory contention.

Answer

Increase the schedule frequency to run the job more often with smaller data increments.

Answer

Replace AWS Glue with Amazon EMR using Spark.

Question 3

Which TWO AWS services can be used to transform data in a streaming fashion without using a persistent cluster? (Choose 2.)

Accepted Answer

AWS Lambda. Option A (Lambda) and Option D (Kinesis Data Analytics) are serverless streaming transformation services. Option B (Glue) is serverless but not low-latency streaming. Option C (EMR) requires a cluster. Option E (Data Pipeline) is for batch.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Data Pipeline

Question 4

Which THREE factors should a data engineer consider when choosing between Amazon S3 and Amazon Redshift for storing large datasets used for machine learning? (Choose 3.)

Accepted Answer

Query performance and latency requirements. Options A, C, and E are key considerations. Option B is about data retention, not storage choice. Option D is about security, which both services support.

Answer

Encryption at rest capabilities

Answer

Data retention policies

Question 5

A company is using Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function writes results to an S3 bucket. The team wants to ensure that each record is processed exactly once and in order. Which TWO configurations should the team implement? (Choose 2.)

Accepted Answer

Set the parallelization factor to 1. Option B (Enable parallelization factor) and Option E (Use S3 bucket versioning) are correct. Option A is wrong because batch size does not affect ordering. Option C is wrong because concurrency limits don't ensure ordering. Option D is wrong because DLQ does not affect ordering.

Answer

Set the batch size to 1

Answer

Increase the Lambda function's reserved concurrency

Answer

Configure a dead-letter queue for failed records

Question 6

Refer to the exhibit. An ML engineer applies this bucket policy to an S3 bucket. The SageMaker execution role MySageMakerRole is used to train a model. The training data is located in s3://my-bucket/data/. The SageMaker training job fails with an access error. What is the most likely cause?

Accepted Answer

The Deny statement on s3:ListBucket prevents the role from listing objects in the bucket.. Option A is correct because the Deny statement explicitly denies the ListBucket action to all principals, including the SageMaker role. Even though GetObject is allowed, SageMaker often needs to list objects to read data from a prefix. Option B is wrong because the role is in the same account. Option C is wrong because the bucket is not encrypted. Option D is wrong because the policy does not restrict GetObject.

Answer

The policy allows GetObject only from the data/ prefix, but the training job uses a different prefix.

Answer

The role is not in the same AWS account as the bucket.

Answer

The bucket has default encryption enabled, causing a conflict.

Question 7

Refer to the exhibit. An ML engineer runs the above CLI command to inspect files in an S3 bucket. The training data consists of 200 CSV files, each 1 GB. The engineer plans to use Amazon SageMaker to train a model using this data. What should the engineer do to optimize training performance?

Accepted Answer

Convert the CSV files to Parquet format and use Pipe input mode.. Option D is correct because converting to Parquet and using a Pipe input mode reduces I/O and improves throughput. Option A is wrong because copying to EBS is not efficient. Option B is wrong because Athena is for querying, not training. Option C is wrong because increasing instances may help but does not address the inefficiency of CSV format.

Answer

Increase the number of training instances to process files in parallel.

Answer

Use Amazon Athena to transform the data into CSV format with headers.

Answer

Use the File input mode and copy all files to the training instance's EBS volume.

Question 8

A company runs a real-time recommendation system that uses Amazon SageMaker endpoints for inference. The system ingests user activity data from a mobile app via Amazon API Gateway and AWS Lambda, which writes events to an Amazon Kinesis Data Stream. A second Lambda function consumes the stream, calls a SageMaker endpoint to generate recommendations, and stores the results in Amazon DynamoDB. The system has been working well, but recently the team noticed an increase in latency from the time a user action occurs to when the recommendation is stored. The SageMaker endpoint shows increased invocation latency but no throttling. CloudWatch metrics show that the Kinesis stream's IteratorAgeMilliseconds is increasing, indicating the consumer is falling behind. The Lambda consumer's duration is within limits, but the number of invocations is lower than expected. The team suspects the issue is with the event source mapping. Which course of action should the team take to reduce the latency?

Accepted Answer

Increase the batch size in the event source mapping to process more records per invocation.. Option B is correct because the consumer is falling behind despite adequate Lambda duration, suggesting that the batch size or parallelization factor is too low. Increasing the batch size allows each Lambda invocation to process more records, increasing throughput. Option A is wrong because increasing shards increases cost and may not help if the consumer is the bottleneck. Option C is wrong because reducing concurrency would worsen the situation. Option D is wrong because the Lambda function is already consuming from Kinesis; using Firehose would not directly solve the consumer lag.

Answer

Increase the number of shards in the Kinesis data stream to increase parallelism.

Answer

Decrease the Lambda function's reserved concurrency to force it to scale down.

Answer

Replace the Lambda consumer with an Amazon Kinesis Data Firehose delivery stream.

Question 9

A data engineering team needs to process streaming data from thousands of IoT devices. The data must be ingested with low latency and processed in near real-time to detect anomalies. Which AWS service should they use for ingestion?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time streaming data ingestion and can handle large throughput with low latency. Option A is wrong because S3 is for object storage, not streaming. Option C is wrong because Kinesis Data Analytics is for processing. Option D is wrong because Kinesis Data Firehose is for loading data to destinations.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3

Question 10

A company is using AWS Glue to run ETL jobs that process data from an Amazon RDS for PostgreSQL database. The jobs are failing with connection timeouts. The security group for the RDS instance allows inbound traffic from the Glue job's security group. What is the most likely cause?

Accepted Answer

The VPC does not have an S3 VPC endpoint. AWS Glue jobs run in a VPC that requires an S3 VPC endpoint to access the AWS Glue catalog and other services. Without the endpoint, the job cannot connect to Glue's metadata store, causing timeouts. Option A is wrong because the security group is correctly configured. Option C is wrong because the issue is not about IAM permissions for RDS. Option D is wrong because there is no subnet issue mentioned.

Answer

The security group inbound rule is incorrect

Answer

RDS is in a different subnet

Answer

The IAM role does not have permission to access RDS

Question 11

A data scientist needs to run ad-hoc SQL queries on a large dataset stored in Amazon S3 (Parquet format, 2 TB). The queries are interactive and require sub-second response times. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena can query data in S3 using SQL and provides fast interactive query performance, especially with Parquet. Option A is wrong because EMR requires cluster setup. Option C is wrong because Redshift Spectrum is heavier for ad-hoc. Option D is wrong because QuickSight is for visualization.

Answer

Amazon Redshift Spectrum

Answer

Amazon QuickSight

Answer

Amazon EMR with Spark SQL

Question 12

A company is using Amazon Kinesis Data Firehose to load streaming data into Amazon S3. The data is in JSON format, and they want to convert it to Parquet before storage. What should they configure?

Accepted Answer

Enable data format conversion in Firehose and specify a Glue table. Kinesis Data Firehose can convert incoming data to Parquet or ORC using a schema from AWS Glue. Option A is wrong because Lambda can also transform but is not the primary method. Option C is wrong because Kinesis Data Analytics is for processing. Option D is wrong because Athena is for querying.

Answer

Use an AWS Lambda function to transform the data

Answer

Run an AWS Glue ETL job after data is in S3

Answer

Use Kinesis Data Analytics for Apache Flink to convert the format

Question 13

A data engineering team needs to move 10 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The data is currently stored in HDFS. Which service should they use for an efficient transfer?

Accepted Answer

AWS DataSync. AWS DataSync can transfer data from on-premises HDFS to S3 efficiently, with built-in encryption and validation. Option A is wrong because S3 Transfer Acceleration speeds up transfers but requires network. Option C is wrong because Snowball Edge is for offline transfer. Option D is wrong because Kinesis is for streaming, not bulk historical.

Answer

S3 Transfer Acceleration

Answer

Amazon Kinesis Data Streams

Answer

AWS Snowball Edge

Question 14

An e-commerce company uses Amazon DynamoDB as the primary data store for user sessions. They want to run analytics on historical session data using Amazon Athena. What is the recommended approach to export DynamoDB data to S3 in a format optimized for Athena?

Accepted Answer

Use AWS Glue ETL to read from DynamoDB and write to S3 as Parquet. AWS Glue ETL can read from DynamoDB and write to S3 in Parquet format, which is optimal for Athena. Option A is wrong because DynamoDB Streams to Lambda to S3 is complex and not optimal. Option B is wrong because Data Pipeline is legacy. Option D is wrong because Kinesis is for streaming.

Answer

Use AWS Data Pipeline to copy data to S3 as CSV

Answer

Use Amazon Kinesis Data Firehose to stream data from DynamoDB to S3

Answer

Use DynamoDB Streams with AWS Lambda to write to S3 as JSON

Question 15

A data engineer needs to schedule an AWS Glue ETL job to run every hour. Which service should they use for scheduling?

Accepted Answer

Amazon CloudWatch Events. Amazon CloudWatch Events (now part of Amazon EventBridge) can trigger an AWS Glue ETL job on a schedule using a cron or rate expression. This is the native, serverless scheduling service for running jobs at fixed intervals, such as every hour, without needing to manage any infrastructure.

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Step Functions