DEA-C01 Practice Exam 52 — 20 Questions

Question 1

A data engineer is designing a data pipeline that uses AWS Glue to transform data stored in Amazon S3. The transformation logic must be written in Python and should handle schema evolution automatically. Which THREE features or configurations should the engineer use? (Select THREE.)

Accepted Answer

Use `applyMapping` transformations. Correct options: A, B, C. AWS Glue DynamicFrames handle schema evolution automatically. Glue's schema detection can infer schema from data. Using `applyMapping` allows for schema transformations. Option D (Spark SQL) does not handle schema evolution automatically. Option E (Glue crawlers) are for cataloging, not transformation.

Answer

Schedule a Glue crawler to update the schema

Answer

Use Spark SQL for transformations

Question 2

Which TWO AWS services can be used to ingest streaming data into Amazon S3 with minimal code? (Choose two.)

Accepted Answer

Amazon Kinesis Data Firehose. Option A (Kinesis Data Firehose) and Option D (Amazon MSK with S3 connector) are correct. Option B (Lambda) requires code. Option C (DMS) is for databases. Option E (DataSync) is for batch file transfers.

Answer

AWS Lambda

Answer

AWS Database Migration Service (DMS)

Answer

AWS DataSync

Question 3

A data engineering team uses AWS Glue ETL jobs to process data daily. They notice that job run times are increasing as data volume grows. Which action will most effectively improve performance without changing the code?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs (Data Processing Units) allocated to the job provides more parallelism and memory, speeding up processing without code changes.

Answer

Use a smaller instance type for the Glue job.

Answer

Enable job bookmark to skip previously processed data.

Answer

Split the data into more files in S3.

Question 4

A data engineer reviews the Glue job configuration. The job fails when processing large datasets. The error message indicates out-of-memory in the executors. Which change to the job configuration will most directly address this issue?

Accepted Answer

Change the worker type from Standard to G.2X.. The job uses 5 workers of Standard type. To increase memory, the engineer should use G.1X or G.2X worker types which provide more memory per worker, or increase the number of workers. Among options, changing worker type to G.2X directly increases memory.

Answer

Increase the timeout from 30 to 60 minutes.

Answer

Increase the number of workers from 5 to 10.

Answer

Set MaxRetries to 3.

Question 5

A company uses AWS Glue to process large datasets. The Glue job occasionally fails with 'DiskFull' errors. Which TWO actions should the engineer take to resolve this issue? (Choose two.)

Accepted Answer

Increase the number of workers for the Glue job.

Answer

Store intermediate results in Amazon S3 instead of local disk.

Answer

Enable job bookmark to skip already processed data.

Answer

Use a Python shell job instead of Spark.

Question 6

A data engineer is using AWS Glue to run an ETL job that reads data from Amazon DynamoDB and writes to Amazon Redshift. The job fails with a 'ThroughputExceededException' error. What is the most likely cause?

Accepted Answer

The DynamoDB table's read capacity is insufficient for the Glue job's read rate. Option A is correct because DynamoDB throttles requests when read/write capacity is exceeded. The Glue job may be reading too fast for the table's provisioned capacity. Option B is wrong because Redshift concurrency scaling does not cause this error. Option C is wrong because Glue job timeout would be a different error. Option D is wrong because S3 permissions would be a different error.

Answer

The Glue job has a timeout setting that is too low

Answer

The Redshift cluster's concurrency scaling is insufficient

Answer

The S3 bucket where Glue writes temporary data does not have proper permissions

Question 7

A company uses AWS Glue ETL to transform data from Amazon RDS for PostgreSQL to Amazon S3. The transformation includes joining several tables and aggregating millions of rows. The job runs successfully but takes over 2 hours. The data engineer wants to reduce runtime. Which action is MOST effective?

Accepted Answer

Increase the number of DPUs for the Glue job.. Option D is correct because increasing the number of DPUs (Data Processing Units) in AWS Glue ETL jobs can significantly reduce runtime by parallelizing the transformation. Option A is wrong because Auto Scaling is enabled by default and may not help if DPUs are already maxed. Option B is wrong because converting to Parquet can help with reading from S3, but the source is RDS, not S3. Option C is wrong because writing to S3 using dynamic frames does not directly improve performance of reading from RDS.

Answer

Enable Auto Scaling for the Glue job.

Answer

Use AWS Glue DynamicFrames instead of DataFrames.

Answer

Convert the source data to Parquet format.

Question 8

A company wants to ingest real-time streaming data from thousands of IoT devices into AWS for immediate processing. Which service is designed for ingesting large volumes of streaming data with low latency?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is purpose-built for real-time streaming data ingestion at scale. Option A (SQS) is pull-based and not optimized for streaming; Option C (S3) is object storage; Option D (DMS) is for database migration.

Answer

Amazon Simple Storage Service (S3)

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Simple Queue Service (SQS)

Question 9

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Streams. The data must be transformed in real-time and then stored in Amazon S3 for long-term retention. Which THREE services can be used together to achieve this?

Accepted Answer

Amazon Kinesis Data Analytics. Options B, C, and D are correct: Kinesis Data Streams ingests, Kinesis Data Analytics transforms, and Kinesis Data Firehose writes to S3. Option A (Glue) is batch-oriented, not real-time. Option E (Athena) is for querying, not streaming.

Answer

AWS Glue

Answer

Amazon Athena

Question 10

A data engineer is troubleshooting an AWS Glue ETL job that fails with an OutOfMemory error when processing large JSON files from Amazon S3. The files contain deeply nested structures. Which approach should the engineer take to resolve this issue?

Accepted Answer

Increase the DPU (Data Processing Unit) per worker. Option D is correct because using the `recurse` option with `getResolvedOptions` is not relevant; the issue is memory. Option A increases parallelism but not memory per worker. Option B reduces memory. Option C (increase number of workers) adds more workers but each still has the same memory; option D (increase DPU per worker) allocates more memory per worker, solving the OOM issue.

Answer

Use the `recurse` option with `getResolvedOptions` to limit recursion

Answer

Increase the number of workers in the Glue job configuration

Answer

Decrease the number of partitions while reading the data

Question 11

A data engineer needs to ingest on-premises CSV files into Amazon S3 every hour. The files are less than 1 GB each. Which service is the most cost-effective and requires the least operational overhead?

Accepted Answer

AWS DataSync. Option B is correct because AWS DataSync is designed for scheduled transfers from on-premises to S3 with minimal setup. Option A is wrong because Snowball is for large, offline transfers. Option C is wrong because Kinesis is for streaming data. Option D is wrong because DMS is for database migrations.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Snowball Edge

Answer

AWS Database Migration Service (DMS)

Question 12

Which TWO actions can improve the performance of an AWS Glue ETL job that processes large datasets in Amazon S3? (Choose two.)

Accepted Answer

Increase the number of DPUs allocated to the job.. Option A (increase DPUs) and Option D (use columnar format) are correct. Option B (use a single file) may reduce parallelism. Option C (increase crawler frequency) does not affect ETL performance. Option E (single availability zone) does not improve performance.

Answer

Increase the frequency of the Glue crawler.

Answer

Use a single Availability Zone for the S3 bucket.

Answer

Use a single large file instead of many small files.

Question 13

A company ingests millions of small files (1-10 KB) into Amazon S3 every hour. These files are then processed by AWS Glue ETL jobs. The Glue jobs are slow because of the overhead of reading many small files. Which strategy will most effectively improve Glue job performance?

Accepted Answer

Use a Lambda function to merge small files into larger ones before Glue processes them.. Grouping small files into larger ones (e.g., by merging in a preprocessing step) reduces the number of file read operations and improves Glue's efficiency. Using S3 Select or increasing DPUs helps but doesn't address the root cause.

Answer

Enable Glue job bookmark.

Answer

Increase the number of DPUs for the Glue job.

Answer

Use S3 Select to filter data before Glue reads it.

Question 14

A data engineering team needs to ingest streaming data from thousands of IoT devices. The data must be processed in near real-time and stored in Amazon S3 in Apache Parquet format partitioned by device_id and timestamp. Which combination of services should the team use to minimize operational overhead and cost?

Accepted Answer

Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, and optionally AWS Lambda.. Option D is correct. Kinesis Data Streams ingests streaming data, Kinesis Data Firehose delivers data to S3 with built-in Parquet conversion and partitioning, and optionally uses Lambda for lightweight transformations. Option A is wrong because EC2-based ingestion is high overhead. Option B is wrong because S3 Transfer Acceleration is for large file transfers, not streaming. Option C is wrong because Kafka and Flink require more operational overhead.

Answer

Amazon Kinesis Data Streams, Amazon EC2 for processing, and Amazon S3 with lifecycle policies.

Answer

Amazon MSK (Kafka), AWS Glue Streaming, and Amazon S3.

Answer

Amazon S3 Transfer Acceleration and AWS Lambda for event-driven transformation.

Question 15

A company uses AWS DMS to continuously replicate data from an on-premises SQL Server to Amazon Aurora MySQL. The replication lag is increasing. Which THREE actions can reduce the lag? (Choose three.)

Accepted Answer

Use parallel apply on the target endpoint.

Answer

Enable DMS validation.

Answer

Enable Multi-AZ for the DMS replication instance.

Question 16

A data engineer is designing a data ingestion pipeline that uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The engineer wants to ensure that the data is organized in a directory structure by year, month, day, and hour. Which TWO configurations should the engineer set on the Firehose delivery stream? (Choose TWO.)

Accepted Answer

Enable dynamic partitioning. Correct options: A and D. Option A is correct because enabling dynamic partitioning allows Firehose to use partition keys from the data. Option D is correct because custom prefix configuration allows specifying the directory structure with expressions like '!{timestamp:yyyy}/!{timestamp:MM}/...'. Option B is wrong because S3 objects have keys, not directories; the prefix defines the path. Option C is wrong because Lambda transformation is not required for partitioning. Option E is wrong because data format conversion is separate from partitioning.

Answer

Use an AWS Lambda function to write to S3 with the desired prefix

Answer

Enable format conversion to Parquet

Answer

Configure an S3 bucket with versioning enabled

Question 17

A company needs to ingest real-time sensor data from thousands of IoT devices into Amazon S3, with a latency of less than 1 minute. The data must be transformed (e.g., convert to Parquet) before landing in S3. Which combination of services is MOST cost-effective?

Accepted Answer

Amazon Kinesis Data Streams to Amazon Kinesis Data Firehose to S3.. Option A is correct because Kinesis Data Streams ingests data in real-time, and Kinesis Data Firehose can buffer, transform (e.g., convert to Parquet), and deliver to S3 with low latency. Option B is wrong because Kinesis Data Analytics is for running SQL on streams, not for simple transformation. Option C is wrong because Glue is batch-oriented, not real-time streaming. Option D is wrong because Lambda can transform but may not scale cost-effectively for thousands of devices.

Answer

Amazon Kinesis Data Streams to AWS Lambda to S3.

Answer

Amazon Kinesis Data Streams to Amazon Kinesis Data Analytics to S3.

Answer

Amazon Kinesis Data Streams to AWS Glue streaming ETL to S3.

Question 18

A company ingests JSON data from an S3 bucket into a Glue ETL job. The data contains nested structures and arrays. The team wants to flatten the data into a tabular format for analysis in Athena. Which Glue transformation is appropriate?

Accepted Answer

Relationalize. The Relationalize transformation is specifically designed to flatten nested JSON into relational tables. Option A (DropNullFields) removes nulls; Option C (Map) applies a function; Option D (Filter) selects rows.

Answer

Map

Answer

Filter

Answer

DropNullFields

Question 19

A data pipeline uses AWS Glue to process large CSV files. The team notices that some jobs fail with out-of-memory errors. Which TWO configuration changes can help mitigate this issue?

Accepted Answer

Increase the number of DPUs for the Glue job.. Options A and D are correct: increasing DPUs and enabling autoscaling provide more memory. Option B (reducing DPUs) would worsen the problem. Option C (conversion to Parquet) may reduce memory but is not a direct configuration change for the Glue job. Option E (job bookmarks) does not affect memory.

Answer

Reduce the number of DPUs to limit concurrency.

Answer

Convert input files from CSV to Parquet.

Answer

Enable job bookmarks.

Question 20

A data engineer is ingesting data from a third-party API into Amazon S3 using AWS Lambda. The API returns a JSON payload of up to 10 MB per request. The Lambda function runs every minute. Occasionally, the function times out after 15 seconds. What is the most likely cause?

Accepted Answer

The Lambda function's timeout setting is too short.. Option B is correct because Lambda has a default timeout of 3 seconds, but can be increased to 15 minutes. If the function times out at 15 seconds, the timeout is set to 15 seconds. Increasing it resolves the issue. Option A is wrong because 10 MB is within Lambda limits. Option C is wrong because memory may need increase but timeout is the immediate cause. Option D is wrong because VPC configuration can cause delays but not specifically timeout.

Answer

The Lambda function is in a VPC without a NAT gateway, causing network timeouts.

Answer

The Lambda function's memory is too low, causing slow processing.

Answer

The API response size exceeds the Lambda invocation payload limit.