DEA-C01 Practice Test 42 — 15 Questions

Question 1

A company is using AWS DMS to replicate data from an on-premises Oracle database to Amazon RDS for MySQL. The replication is working, but the target table has a different schema. Which DMS feature should be used to transform the source schema to match the target?

Accepted Answer

Use DMS transformation rules. Option A is correct because DMS supports transformation rules that can change table names, schemas, and columns during migration. Option B is wrong because DMS does not have a built-in schema conversion feature (that's AWS SCT). Option C is wrong because Lambda can be used for custom transformations but adds complexity. Option D is wrong because Glue is a separate ETL service, not integrated with DMS.

Answer

Use AWS Schema Conversion Tool (SCT)

Answer

Use AWS Glue ETL jobs

Answer

Use AWS Lambda triggers

Question 2

A company uses Amazon Kinesis Data Streams to ingest IoT sensor data. The data is processed by an AWS Lambda function that transforms the records and writes to an Amazon S3 bucket. Recently, the Lambda function has been failing with 'Rate exceeded' errors for the S3 PUT API calls. The data volume is 10 MB/s with average record size 2 KB. What should be done to resolve this issue?

Accepted Answer

Add a random prefix to the S3 object key to distribute writes across multiple prefixes. Option A is correct because S3 has a PUT request rate limit of 3,500 objects/s per prefix. With 10 MB/s and 2 KB records, that's 5,000 records/s, exceeding the limit. Increasing the number of S3 prefixes distributes writes across multiple partitions. Option B (increase Lambda concurrency) would worsen the issue. Option C (increase Kinesis shards) doesn't address S3 throttling. Option D (use Firehose) may help but is a bigger change; adding prefixes is simpler.

Answer

Switch to Amazon Kinesis Data Firehose to write to S3

Answer

Increase the Lambda function's reserved concurrency

Answer

Increase the number of Kinesis shards

Question 3

A company is streaming IoT data from thousands of devices into Amazon Kinesis Data Streams. The data must be transformed in real time before being stored in Amazon S3. Which service should be used to perform the transformation as the data streams through Kinesis?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Option A is correct because Amazon Kinesis Data Analytics for Apache Flink can process streaming data in real time using Flink, which is ideal for transformations. Option B (Lambda) can process records but is better for lightweight transformations and may not handle complex stateful operations. Option C (Glue) is batch-oriented, not real-time. Option D (EMR) is for big data processing but adds latency.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Lambda

Question 4

A company is ingesting streaming data from social media feeds using Amazon Kinesis Data Streams. The data is consumed by multiple applications: one for real-time sentiment analysis and another for archival to S3. The data must be processed in order for each social media post. Which TWO approaches meet the requirements? (Choose TWO.)

Accepted Answer

Use a single shard in the Kinesis Data Streams and have all consumers read from that shard. Option A is correct because using a single shard ensures ordering for all records. Option C is correct because using a partition key that groups related records ensures they go to the same shard, preserving order. Option B (multiple shards) does not guarantee global ordering. Option D (Firehose) does not guarantee ordering. Option E (SQS FIFO) can guarantee order but adds another service.

Answer

Use Amazon Kinesis Data Firehose to buffer and deliver to S3

Answer

Use Amazon SQS FIFO queues between the stream and consumers

Answer

Use multiple shards and assign each consumer to a specific shard

Question 5

A data engineer needs to transform JSON data from a Kinesis Data Stream into Parquet format and store it in an S3 data lake. The transformation includes simple field mapping and data type conversions. Which AWS service is the most cost-effective for performing this transformation in near-real-time?

Accepted Answer

AWS Lambda function triggered by Kinesis Data Firehose. Option D is correct because Kinesis Data Firehose can perform data transformation using a Lambda function before delivering to S3, and it supports converting JSON to Parquet using its built-in capability or Lambda. Option A is wrong because AWS Glue is a serverless Spark ETL service, but it is designed for batch processing, not streaming. Option B is wrong because Amazon EMR is a managed Hadoop cluster that is more complex and costly for simple transformations. Option C is wrong because Amazon Athena is an interactive query service, not a transformation engine.

Answer

Amazon Athena with CTAS (CREATE TABLE AS SELECT)

Answer

AWS Glue ETL job

Answer

Amazon EMR with Spark Streaming

Question 6

A company uses AWS Glue to process data from multiple sources. The data is stored in an Amazon S3 data lake. The company needs to transform the data using a custom Python library that is not available in the default Glue environment. What is the MOST efficient way to make this library available to the Glue jobs?

Accepted Answer

Upload the library as a .whl file to Amazon S3 and reference it in the Glue job's --additional-python-modules parameter.. Option D is correct because AWS Glue supports Python shell jobs and allows installing additional Python modules via --additional-python-modules or by providing a .whl file in S3 and referencing it. Option A is wrong because Glue does not support installing packages via pip at runtime. Option B is wrong because creating a custom Docker image is for Glue for Ray or for ETL jobs with custom environment, but it's more complex than needed. Option C is wrong because installing the library on every node is not supported; Glue manages the environment.

Answer

Manually install the library on each node in the Glue cluster by editing the bootstrap script.

Answer

Create a custom Docker image with the library and use it in AWS Glue for Ray.

Answer

Use a shell command in the Glue job script to run 'pip install <library>' before the job runs.

Question 7

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and must be queryable using Amazon Athena. The engineer needs to optimize query performance and reduce costs. Which THREE actions would achieve this?

Accepted Answer

Partition the data by a commonly used filter column.. Option A: Partitioning reduces data scanned. Option C: Using columnar formats like Parquet reduces data scanned. Option E: Compression reduces storage and data scanned. Option B is wrong because the number of files should be optimized, not increased. Option D is wrong because S3 Select is for filtering within a single file, not for Athena.

Answer

Store data in many small files to increase parallelism.

Answer

Use S3 Select instead of Athena for queries.

Question 8

A data engineer is using Amazon EMR to transform large datasets stored in S3. The cluster runs once a day and takes 3 hours. The engineer notices that the cluster is idle for 30 minutes at the start while waiting for resources. What is the most cost-effective way to reduce the idle time?

Accepted Answer

Configure a larger initial core instance count and enable managed scaling. Option C is correct because using a cluster with managed scaling and a larger initial core node count can reduce the time waiting for resource allocation. Option A (spot instances) may cause interruptions. Option B (reserved instances) is for long-term use, not transient clusters. Option D (increasing instance size) may reduce processing time but not idle time.

Answer

Increase the instance type size

Answer

Use Spot Instances for all nodes

Answer

Purchase Reserved Instances for the cluster

Question 9

A company wants to ingest data from an on-premises Oracle database into Amazon S3 on a daily basis. The data volume is 500 GB per transfer. Which AWS service is most appropriate for this batch ingestion?

Accepted Answer

AWS Glue. Option B is correct because AWS Glue can run scheduled ETL jobs to extract data from JDBC sources like Oracle and write to S3. Option A (DMS) is for ongoing replication, not scheduled batch. Option C (Firehose) is for streaming, not batch. Option D (Data Pipeline) is a legacy service.

Answer

AWS Database Migration Service (DMS)

Answer

AWS Data Pipeline

Answer

Amazon Kinesis Data Firehose

Question 10

A company is using AWS Database Migration Service (DMS) to migrate a 2 TB Oracle database to Amazon Aurora PostgreSQL. The migration must have minimal downtime. The source database is highly active with continuous writes. Which DMS migration type and additional configuration should the engineer use?

Accepted Answer

Use a full load migration task followed by ongoing replication (CDC).. Option D is correct because full load + change data capture (CDC) allows initial bulk load and then continuously replicates ongoing changes to keep the target in sync, minimizing downtime. Option A is wrong because full load only will not capture changes during migration, causing data loss. Option B is wrong because CDC only requires an initial snapshot anyway; starting with CDC assumes existing data is already present, which isn't the case. Option C is wrong because task restart does not address ongoing changes.

Answer

Use a CDC-only migration task to capture changes from the source.

Answer

Use a full load migration task and stop the source database before starting.

Answer

Use a full load migration task with task restart enabled.

Question 11

A company uses AWS Glue to run ETL jobs daily. The jobs consume data from an Amazon RDS for MySQL database and write results to Amazon S3. The company wants to minimize the impact on the source database during extraction. Which THREE actions should the data engineer take to achieve this? (Choose THREE.)

Accepted Answer

Schedule the Glue job to run during off-peak hours.. Options A, C, and D are correct. Option A: Using a read replica avoids load on the primary database. Option C: Using JDBC connection with a WHERE clause to extract only new/changed data reduces the amount of data read. Option D: Scheduling Glue jobs during off-peak hours minimizes impact. Option B is wrong because increasing DPUs does not reduce database load; it may increase parallelism and actually increase load. Option E is wrong because reducing job bookmarks would cause full scans, increasing load.

Answer

Increase the number of Glue DPUs to process data faster.

Answer

Disable Glue job bookmarks to force full refresh.

Question 12

A company is using AWS Glue ETL jobs to process data from Amazon S3 and write results back to S3. The jobs are failing intermittently with 'ThrottlingException' errors. Which TWO configurations would help reduce these errors?

Accepted Answer

Add retry logic with exponential backoff in the job script.. Option B: Increasing the number of DPUs may distribute the load and reduce throttling. Option D: Implementing retry logic in the job handles transient throttling. Option A is wrong because it increases the chance of throttling. Option C is wrong because compression doesn't reduce API calls. Option E is wrong because the job type is not related.

Answer

Decrease the number of DPUs for the job.

Answer

Enable GZIP compression on the output data.

Answer

Change the job type from Spark to Python shell.

Question 13

A company needs to ingest data from an on-premises Hadoop cluster into Amazon S3 for archival and analysis. The total data volume is 50 TB. The migration must be completed within one week. The on-premises network has a 1 Gbps connection to AWS. Which AWS service should be used?

Accepted Answer

AWS Snowball Edge. Option C is correct because AWS Snowball is a physical device that can transfer large amounts of data faster than over the network given the bandwidth limitation. Option A (DataSync) can use the network but may not complete within a week due to bandwidth constraints. Option B (Glue) is for ETL, not raw transfer. Option D (Transfer Family) is for SFTP.

Answer

AWS Transfer Family

Answer

AWS Glue

Answer

AWS DataSync

Question 14

A data engineer runs an AWS Glue ETL job that reads from a large Amazon S3 source (several terabytes of CSV files) and writes transformed data to an S3 bucket in Parquet format. The job fails with the error shown in the exhibit. The job uses the Standard worker type with 10 workers (G.1X). The engineer needs to resolve the failure with minimal cost increase. What should the engineer do?

Accepted Answer

Change the worker type to G.2X with 10 workers.. Option D is correct because the OutOfMemoryError indicates that the Spark executors do not have enough memory; switching to G.2X workers doubles memory per worker (from 16 GB to 32 GB) without increasing the number of workers, which is more cost-effective than increasing the number of workers with G.1X. Option A is wrong because increasing the number of workers with G.1X may not resolve the memory issue per executor; it spreads the data across more executors but each still has limited memory. Option B is wrong because reducing the number of files (coalesce) may not help if the issue is per-task memory. Option C is wrong because using the G.2X worker type with more memory per worker is likely sufficient; G.4X may be unnecessary and more expensive.

Answer

Increase the number of workers to 20 while keeping G.1X worker type.

Answer

Change the worker type to G.4X with 10 workers.

Answer

Set the 'coalesce' parameter to reduce the number of output files.

Question 15

A data engineer is designing a data ingestion pipeline for real-time clickstream data from a website. The data must be ingested with low latency (seconds) and made available for multiple consumer applications, including a dashboard that refreshes every minute and a machine learning model that processes data in near-real-time. The engineer needs to choose a streaming ingestion service. Which TWO services meet these requirements? (Select TWO.)

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Option A (Kinesis Data Streams) provides low-latency ingestion and allows multiple consumers to process data independently via enhanced fan-out. Option D (Amazon MSK) is also a streaming platform that supports multiple consumers with low latency. Option B (Kinesis Data Firehose) is designed for loading data into destinations with buffering, not for multiple consumers. Option C (SQS) is a message queue with at-least-once delivery but is not a streaming platform and does not support replay efficiently. Option E (S3) is object storage, not a streaming ingestion service.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon S3