MLS-C01 Data Engineering Practice Test 21 — 15 Questions

Question 1

An AWS Glue ETL job failed with the error 'Insufficient memory allocated for the job'. The job run details show AllocatedCapacity: 5, WorkerType: Standard, NumberOfWorkers: 5. Which change should be made to resolve the issue?

Accepted Answer

Increase the number of workers to 10. Option C is correct because the error indicates insufficient memory; increasing the number of workers (DPUs) provides more memory. Option A is wrong because worker type Standard has 16 GB memory; G.2X has 32 GB, but the error is about allocated capacity, not worker type. Option B is wrong because job timeout is not the issue. Option D is wrong because starting a new job won't fix the resource allocation.

Answer

Delete and recreate the job with a different name

Answer

Increase the job timeout to 3600 minutes

Answer

Change the worker type to G.2X

Question 2

An S3 event notification triggers an AWS Lambda function when a new object is created. The Lambda function parses the event and processes the object. The function is failing with a timeout error for large objects. Which approach should be used to handle large objects efficiently?

Accepted Answer

Use an SQS queue to buffer event notifications and configure Lambda with a batch window. Option B is correct because S3 event notifications can be sent to an SQS queue, and Lambda can process messages in batches with longer timeouts. Option A is wrong because increasing timeout alone doesn't solve the issue of large objects; Option C is wrong because Kinesis is not needed; Option D is wrong because Step Functions adds complexity.

Answer

Increase the Lambda function timeout to 15 minutes

Answer

Stream events to Amazon Kinesis Data Streams and process with Lambda

Answer

Use AWS Step Functions to orchestrate the processing

Question 3

A data engineer needs to transform large CSV files stored in Amazon S3 into Parquet format before loading into Amazon Redshift. The transformation logic is complex and requires custom Python code. Which AWS service should be used to perform this transformation with minimal operational overhead?

Accepted Answer

AWS Glue. Option B is correct because AWS Glue provides a serverless Spark environment with built-in support for ETL jobs, including converting CSV to Parquet. Option A (AWS Lambda) has a 15-minute timeout and is not suitable for large files. Option C (Amazon EMR) requires managing clusters. Option D (AWS Data Pipeline) is a legacy service with less flexibility.

Answer

AWS Lambda

Answer

Amazon EMR

Answer

AWS Data Pipeline

Question 4

A company is streaming clickstream data from a website to Amazon Kinesis Data Streams. The data is consumed by a Lambda function that enriches each record with geolocation information before writing to an S3 bucket. Recently, the Lambda function has been failing with throttling errors. What is the MOST likely cause?

Accepted Answer

The Lambda function's concurrent execution limit has been reached. Option D is correct because the default Lambda concurrent execution limit (1000) can be reached if the stream has many shards or high throughput. Option A (shard limit) is a Kinesis limit, not Lambda. Option B (buffer size) is not a Lambda issue. Option C (record size) would cause a different error.

Answer

The Lambda function's payload size exceeds the 6 MB limit

Answer

The Lambda function's reserved concurrency is set too high

Answer

The Kinesis stream has exceeded the default shard limit of 500

Question 5

A data scientist wants to run a one-time SQL query on a large dataset stored in Amazon S3 (CSV format, 2 TB) using Amazon Athena. The query involves joining this dataset with a smaller table stored in Amazon RDS. What is the MOST cost-effective and performant approach?

Accepted Answer

Export the RDS table to S3 in Parquet format, then use Athena to join the two S3 datasets. Option A is correct. Exporting the RDS table to S3 as Parquet and running the join in Athena avoids data transfer costs and leverages Athena's fast query engine. Option B (federated query) adds complexity and may be slower. Option C (Redshift Spectrum) requires a Redshift cluster. Option D (Glue ETL) is overkill for a one-time query.

Answer

Use Amazon Redshift Spectrum to query both S3 and RDS

Answer

Use Athena Federated Query to query RDS directly

Answer

Use AWS Glue ETL to join the data and write results back to S3, then query with Athena

Question 6

A company is using Amazon Kinesis Data Firehose to load streaming data into an S3 bucket. The data schema evolves over time, with new columns added. The data must be queryable using Amazon Athena. What is the BEST way to handle schema changes?

Accepted Answer

Use AWS Glue Crawlers to automatically detect schema changes and update the table metadata. Option D is correct. Using Glue Crawlers to update the schema and partitioning by date allows Athena to handle schema evolution gracefully. Option A (schema-on-read) is how Athena works, but manual updates are not needed. Option B (convert to JSON) is not necessary. Option C (recreate table) is disruptive.

Answer

Manually update the Athena table definition each time a new column is added

Answer

Configure Firehose to convert the data to Apache JSON format

Answer

Recreate the Athena table daily to pick up new columns

Question 7

A company runs a daily batch ETL job using AWS Glue that reads from Amazon RDS (MySQL), transforms the data, and writes to Amazon Redshift. The job takes 6 hours and processes 500 GB of data. Management wants to reduce the runtime. Which action would be MOST effective?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Option B is correct. Increasing the number of DPUs (Data Processing Units) in the Glue job can parallelize the workload and reduce runtime. Option A (increase Redshift node size) helps only at the write stage. Option C (SSD on RDS) is not a bottleneck. Option D (using COPY command) is for Redshift, not Glue.

Answer

Increase the node size of the Redshift cluster

Answer

Use the Redshift COPY command to load data directly from RDS

Answer

Use Amazon RDS with Provisioned IOPS SSD storage

Question 8

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time sensor data. The application reads from a Kinesis data stream, performs windowed aggregations, and writes results to an S3 bucket. Recently, the application has been experiencing high latency and checkpoint failures. What is the MOST likely cause?

Accepted Answer

The S3 destination bucket is located in a different AWS Region than the Kinesis application. Option C is correct. If the S3 bucket is in a different region, cross-region data transfer can introduce latency and checkpoint failures. Option A (parallelism) would cause resource issues, not necessarily checkpoint failures. Option B (shard count) would cause throttling. Option D (record size) is limited to 1 MB.

Answer

The number of shards in the Kinesis stream is insufficient for the data volume

Answer

The record size in the Kinesis stream exceeds the 1 MB limit

Answer

The parallelism of the Flink application is set too low for the number of shards

Question 9

A data engineer needs to ingest data from an on-premises Apache Kafka cluster into Amazon S3 with minimal latency (under 5 minutes) for real-time analytics. The data volume is approximately 10 MB per second. Which solution is MOST cost-effective and meets the latency requirement?

Accepted Answer

Use Amazon MSK to mirror the on-premises Kafka cluster, then use Kinesis Firehose to write to S3. Option B is correct. Amazon MSK (Managed Streaming for Kafka) can replicate the on-premises Kafka topics to the cloud with low latency, and then Kinesis Firehose can deliver to S3 with a 60-second buffer. Option A (Direct connect + Lambda) has higher latency. Option C (Kinesis Data Streams) requires additional ingestion from on-premises. Option D (S3 Transfer Acceleration) is for file uploads, not streaming.

Answer

Use Amazon S3 Transfer Acceleration for direct uploads from on-premises

Answer

Use Amazon Kinesis Data Streams with a Direct Connect connection from on-premises

Answer

Set up a VPN connection and use AWS Lambda to consume from Kafka and write to S3

Question 10

A company uses Amazon Redshift for data warehousing. The data engineering team needs to load data from multiple S3 buckets into Redshift daily. Each bucket contains files in different formats (CSV, JSON, Parquet). Which AWS service is BEST suited to automate this ingestion process?

Accepted Answer

AWS Glue. Option A is correct. AWS Glue can crawl the S3 buckets to discover schema and run ETL jobs to transform and load data into Redshift. Option B (DMS) is for database migration. Option C (EMR) requires more management. Option D (Data Pipeline) is less flexible.

Answer

Amazon EMR with Apache Spark

Answer

AWS Data Pipeline

Answer

AWS Database Migration Service (DMS)

Question 11

A data engineer is designing a data lake on Amazon S3. The data comes from various sources and must be stored in a way that supports both batch and real-time analytics. The engineer needs to partition the data to optimize query performance in Amazon Athena. Which partitioning strategy is MOST appropriate?

Accepted Answer

Partition by year, then month, then day, then hour. Option C is correct. Partitioning by year/month/day/hour allows efficient querying for both batch (daily) and real-time (hourly) use cases, and is a common practice. Option A (source type) may cause small files. Option B (random) is not helpful. Option D (single partition) defeats the purpose.

Answer

Partition by a hash of the record ID to distribute data evenly

Answer

Do not partition; use a single prefix for all data

Answer

Partition by source type and then by date

Question 12

A company is using Amazon Kinesis Data Streams with 10 shards to ingest clickstream data. Each record is approximately 50 KB. The data is consumed by a Lambda function that writes to DynamoDB. The Lambda function is experiencing throttling errors. Which TWO actions should the data engineer take to resolve the issue? (Choose TWO.)

Accepted Answer

Request a limit increase for the Lambda function's concurrent execution limit. Option A (increase shards) increases throughput but may not solve Lambda throttling. Option B (increase Lambda concurrency limit) directly addresses throttling. Option C (increase batch size) reduces number of Lambda invocations. Option D (use Firehose) changes architecture. Option E (increase record size) is irrelevant. The correct answers are B and C because they reduce the number of concurrent Lambda executions and increase efficiency.

Answer

Increase the record size to 1 MB to reduce the number of records

Answer

Switch to Kinesis Data Firehose instead of Data Streams

Answer

Increase the number of shards in the Kinesis stream

Question 13

A data engineer is designing an ETL pipeline using AWS Glue to process data from Amazon S3 and load it into Amazon Redshift. The pipeline must handle incremental data loads and ensure data consistency. Which THREE features should the engineer use to achieve this? (Choose THREE.)

Accepted Answer

Pushdown predicates to filter partitions in S3. Option A (job bookmark) enables incremental processing. Option C (pushdown predicate) reduces data scanned. Option E (transactional table) ensures consistency. Option B (partition filter) is less efficient. Option D (data preview) is for development.

Answer

Glue data preview to validate transformation logic

Answer

Glue partition filters to limit data scanned

Question 14

A company stores IoT sensor data in Amazon S3 and uses Amazon Athena for ad-hoc queries. The data is partitioned by date, but queries are still slow and expensive. Which TWO actions can improve query performance and reduce cost? (Choose TWO.)

Accepted Answer

Use S3 lifecycle policies to compact small files into larger ones. Option A (convert to Parquet) reduces data scanned. Option C (compact small files) reduces overhead. Option B (increase partitions) can create many small files. Option D (use Glue instead) changes service. Option E (disable encryption) is not related to performance.

Answer

Disable server-side encryption on the S3 bucket

Answer

Use AWS Glue instead of Athena for querying

Answer

Increase the number of partitions to hour-level granularity

Question 15

A data engineer is troubleshooting an AWS Glue job that reads from and writes to the S3 bucket 'data-lake-bucket'. The job fails when trying to write to the 'sensitive/' prefix. The IAM policy attached to the Glue job's IAM role is shown in the exhibit. What is the MOST likely reason for the failure?

Accepted Answer

The IAM role has an explicit deny for s3:PutObject on the 'sensitive/' prefix. Option B is correct. Even though the first statement allows s3:PutObject on the entire bucket, the second statement explicitly denies s3:PutObject on the 'sensitive/' prefix. Explicit deny overrides any allow. Option A is wrong because the policy allows GetObject. Option C is wrong because the policy covers the bucket. Option D is wrong because there is a deny statement.

Answer

The IAM role does not have permission to read objects from the bucket

Answer

The IAM policy does not specify the bucket resource correctly

Answer

The IAM policy lacks a required condition for encryption