MLS-C01 Practice Test 40 — 15 Questions

Question 1

A company uses Amazon EMR to run Spark jobs on a large dataset stored in Amazon S3. The jobs are failing with 'OutOfMemoryError' in the executors. The data is not skewed. Which configuration change will most likely resolve the issue?

Accepted Answer

Increase the spark.executor.memoryOverhead setting. Increasing the executor memory overhead provides additional memory for JVM overhead and can prevent OutOfMemoryError. Option A (increasing cores) may increase parallelism but not memory. Option B (decreasing shuffle partitions) may reduce memory usage but is not a direct fix. Option D (using Kryo serialization) reduces memory usage but is not as effective as increasing overhead.

Answer

Enable Kryo serialization

Answer

Decrease the number of shuffle partitions

Answer

Increase the number of executor cores

Question 2

A company is using Amazon S3 as a data lake. The data engineering team needs to catalog the schema of the data and make it available for querying with Amazon Athena. Which AWS Glue component should be used?

Accepted Answer

AWS Glue Crawlers. AWS Glue Crawlers automatically scan data sources, infer schemas, and populate the AWS Glue Data Catalog. Option B is wrong because Glue ETL jobs are for transforming data. Option C is wrong because Glue DataBrew is for visual data preparation. Option D is wrong because Glue Studio is a visual ETL development tool.

Answer

AWS Glue Studio

Answer

AWS Glue ETL jobs

Answer

AWS Glue DataBrew

Question 3

A data pipeline uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that transforms and writes to Amazon DynamoDB. The Lambda function is throttled during traffic spikes, causing data to be reprocessed. Which solution should the team implement to handle the throttling without losing data?

Accepted Answer

Increase the number of shards in the Kinesis stream and configure a dead-letter queue (DLQ) for the Lambda function.. Option C is correct because increasing the number of shards increases the streaming capacity, and using a DLQ captures failed records for later reprocessing. Option A is wrong because DynamoDB auto scaling does not address Lambda throttling. Option B is wrong because reducing batch size may increase processing overhead. Option D is wrong because SQS is not needed as Kinesis already buffers data.

Answer

Use Amazon SQS as an intermediate buffer between Kinesis and Lambda.

Answer

Enable DynamoDB auto scaling to handle writes.

Answer

Reduce the batch size in the Lambda event source mapping.

Question 4

A data engineering team needs to schedule a nightly ETL job that extracts data from an Amazon RDS for PostgreSQL instance, transforms it using Spark, and loads it into Amazon S3. The team wants to use AWS Glue for this task. Which components are required? (Select TWO.)

Accepted Answer

An AWS Glue ETL job with a Spark script.. Option A is correct because a connection to the PostgreSQL database is needed for extraction. Option D is correct because an AWS Glue ETL job with Spark script performs the transformation. Option B is wrong because a crawler is for cataloging, not for ETL. Option C is wrong because a development endpoint is for interactive development, not production scheduling. Option E is wrong because a notebook is for development, not for scheduled jobs.

Answer

An AWS Glue crawler to populate the Data Catalog.

Answer

An AWS Glue development endpoint.

Answer

An AWS Glue notebook for data exploration.

Question 5

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format, and the company wants to convert it to Parquet for efficient querying. Which configuration should be used?

Accepted Answer

Enable data transformation in Firehose using an AWS Lambda function to convert JSON to Parquet, and set the output format to Parquet.. Option A is correct because Firehose supports data transformation using Lambda and can convert to Parquet via the output format setting. Option B is wrong because Glue is not directly integrated with Firehose. Option C is wrong because Kinesis Data Analytics does not convert to Parquet for S3 delivery. Option D is wrong because Firehose cannot directly write to Redshift in Parquet format without transformation.

Answer

Use an AWS Glue job to convert the JSON files in S3 to Parquet after delivery.

Answer

Use Amazon Kinesis Data Analytics to convert the stream to Parquet before sending to Firehose.

Answer

Configure Firehose to deliver data directly to Amazon Redshift, which automatically converts to Parquet.

Question 6

A team wants to build a data pipeline that processes incoming JSON files from an S3 bucket and loads them into a Redshift table. The pipeline must handle schema evolution and data validation. Which combination of services would be MOST appropriate?

Accepted Answer

Amazon S3 + AWS Glue + Amazon Redshift. AWS Glue can crawl the S3 data to infer schema, perform ETL transformations, and load into Redshift. SQS is not needed. Lambda is event-driven but lacks built-in schema evolution. Data Pipeline is older and less flexible.

Answer

Amazon S3 + Amazon SQS + Amazon Redshift

Answer

Amazon S3 + AWS Data Pipeline + Amazon Redshift

Answer

Amazon S3 + AWS Lambda + Amazon Redshift

Question 7

An S3 event notification is configured to trigger a Lambda function when new objects are created. The Lambda function processes the event JSON shown. Which field should the function use to read the new object from S3?

Accepted Answer

s3.bucket.arn and s3.object.key. The event JSON contains the bucket name under s3.bucket.name and the object key under s3.object.key. The function should use these to construct the S3 URI and read the object. The other fields are not sufficient.

Answer

s3.s3SchemaVersion

Answer

awsRegion

Answer

eventName

Question 8

A data engineer is designing a data ingestion pipeline that will receive up to 5 GB of data per hour from thousands of IoT devices. The data must be stored in Amazon S3 and analyzed in near real-time. Which TWO services should be used together to meet these requirements? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Firehose can ingest streaming data and deliver to S3. Amazon Kinesis Data Analytics can perform near real-time analysis on the data stream. Option A (Amazon SQS) is for decoupling applications, not streaming analytics. Option B (AWS Lambda) can be used for processing but is not a streaming analytics service. Option E (Amazon Athena) is for ad-hoc queries on S3, not real-time.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

Amazon Simple Queue Service (Amazon SQS)

Question 9

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 100 Mbps connection to AWS. The transfer must be completed within one week. Which approach should the engineer use?

Accepted Answer

Use AWS Snowball Edge device to physically transfer the data.. AWS Snowball Edge is a physical device that can handle large data volumes over slow networks. 50 TB over 100 Mbps would take about 50 days, exceeding the one-week requirement. Option B is wrong because AWS DataSync still uses the network. Option C is wrong because S3 Transfer Acceleration improves speed but not enough. Option D is wrong because VPN is not designed for bulk data transfer.

Answer

Use Amazon S3 Transfer Acceleration.

Answer

Use AWS DataSync to transfer the data over the network.

Answer

Use multiple concurrent AWS CLI copy commands over VPN.

Question 10

A data scientist needs to perform exploratory data analysis on a 100 GB CSV file stored in Amazon S3. The data is not sensitive. The scientist wants to use SQL queries to filter and aggregate the data without setting up a server or moving the data. Which service should be used?

Accepted Answer

Amazon Athena. Option B is correct because Amazon Athena is a serverless query service that allows SQL queries directly on data in S3. Option A is wrong because Redshift Spectrum requires a Redshift cluster. Option C is wrong because EMR requires a cluster. Option D is wrong because Glue is for ETL, not ad-hoc querying.

Answer

AWS Glue

Answer

Amazon EMR

Answer

Amazon Redshift Spectrum

Question 11

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 12

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 13

A company uses Amazon EMR with Spark to process data daily. The job reads from S3 and writes to S3. Recently, the job started failing with 'S3AccessDenied' errors. The IAM role used by EMR has not changed. What is the MOST likely cause?

Accepted Answer

The S3 bucket policy was updated to deny access to the EMR role. S3 bucket policies can be changed independently and may block access. IAM policies are not the only factor; bucket policies also apply. The role itself hasn't changed, but the bucket policy might have been updated to deny access. Other options are less likely because the role hasn't changed and the errors are access-related.

Answer

The EMR cluster's security group blocks outbound traffic

Answer

The S3 bucket was deleted and recreated

Answer

The EMR service role was rotated

Question 14

A company runs a daily ETL job that reads data from Amazon RDS, transforms it using AWS Glue, and writes the results to Amazon S3. The job started failing yesterday with the error: 'Rate exceeded'. What is the most likely cause and solution?

Accepted Answer

The RDS database is overwhelmed by the number of connections; reduce the Glue job's parallelism or increase RDS instance size. The 'Rate exceeded' error typically indicates that the job is exceeding the RDS database's maximum connections or IOPS limits. The best solution is to reduce the parallelism or increase the database capacity. Option A (increasing S3 bucket limits) does not address the RDS issue. Option B (increasing Glue DPUs) may exacerbate the issue. Option D (enabling Glue job bookmarks) does not solve rate limiting.

Answer

The Glue job is using too many DPUs; reduce the number of DPUs

Answer

The S3 bucket has reached its request rate limit; request a limit increase

Answer

Enable job bookmarks in the Glue job to process only new data

Question 15

A machine learning team needs to preprocess large volumes of clickstream data stored in Amazon S3 before training a model. The preprocessing includes data cleaning, feature engineering, and normalization. The team wants to use a serverless solution that minimizes operational overhead. Which combination of services should the team use?

Accepted Answer

AWS Glue ETL jobs reading from and writing to S3.. AWS Glue provides a serverless Spark environment for running ETL jobs on data in S3. Amazon SageMaker Processing jobs are also serverless but are more suited for post-training tasks. Option B is wrong because EMR requires cluster management. Option C is wrong because SageMaker Notebooks are interactive, not automated. Option D is wrong because Athena is for ad-hoc queries, not complex transformations.

Answer

Amazon SageMaker Notebooks with custom Python scripts.

Answer

Amazon EMR with Spark clusters.

Answer

Amazon Athena with SQL queries.