DEA-C01 Practice Test 41 — 15 Questions

Question 1

A data engineer needs to ingest data from multiple on-premises relational databases into Amazon S3 for analytics. The data must be transformed and loaded daily. Which THREE AWS services should the engineer use together to build this pipeline? (Choose THREE.)

Accepted Answer

AWS Glue. Options B, C, and D are correct. Option B: AWS DMS can migrate data from on-premises databases to S3. Option C: AWS Glue can transform the data. Option D: AWS Glue Data Catalog can store metadata. Option A is wrong because Kinesis Data Streams is for real-time streaming, not batch ingestion. Option E is wrong because Athena is for querying, not for building the ingestion pipeline.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 2

A company uses AWS Glue to process JSON logs from S3. The logs have a nested structure and the schema evolves over time. The data engineer needs to ensure the Glue job can handle schema changes without failing. Which configuration should be used?

Accepted Answer

Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true. Option B is correct because setting 'mergeDynamicColumns' to true in Glue job parameters allows new columns to be added dynamically. Option A (schema registry) is for schema validation, not evolution. Option C (manual schema updates) is not automated. Option D (Spark SQL) does not solve evolution.

Answer

Manually update the table schema in the Glue Data Catalog before each run

Answer

Use Spark SQL with a static schema definition in the script

Answer

Enable AWS Glue Schema Registry and define a schema version

Question 3

A data engineer needs to ingest data from an on-premises Oracle database to Amazon S3 daily. The data volume is 500 GB per day, and the network bandwidth is 200 Mbps. The requirement is to minimize the impact on the source database and ensure data integrity. Which combination of AWS services should be used?

Accepted Answer

AWS Database Migration Service (DMS) with S3 as target. Option C is correct because AWS DMS can perform ongoing replication with minimal impact, and S3 as a target is supported. Option A (AWS Glue) is a batch ETL tool but may put load on the source. Option B (AWS Data Pipeline) is older and less efficient. Option D (Amazon Kinesis) is for streaming, not batch database loads.

Answer

AWS Glue ETL jobs with JDBC connection

Answer

Amazon Kinesis Data Firehose with Oracle as source

Answer

AWS Data Pipeline with SQLActivity

Question 4

A company is designing a data ingestion pipeline for real-time clickstream data. The data must be ingested with low latency (< 1 second) and then processed for real-time analytics. The processed data should be stored in Amazon S3 for batch analytics. Which THREE services should be used together?

Accepted Answer

Amazon Kinesis Data Analytics. Options A, B, and D are correct. Kinesis Data Streams ingests data with low latency. Kinesis Data Analytics processes streaming data in real-time. Kinesis Data Firehose delivers processed data to S3. Option C (AWS Glue) is batch-oriented. Option E (Amazon MSK) is an alternative but not necessary if Kinesis is used.

Answer

Amazon Managed Streaming for Apache Kafka (MSK)

Answer

AWS Glue ETL job

Question 5

A company is ingesting log files from multiple EC2 instances into Amazon S3 using the CloudWatch agent. The logs are delivered to a CloudWatch Logs group, and a subscription filter sends them to a Lambda function for transformation, then to Firehose. The Firehose stream is configured with a buffer interval of 60 seconds and buffer size of 5 MB. The logs are critical and must be available in S3 within 5 minutes. What is the most cost-effective way to reduce the delivery latency?

Accepted Answer

Decrease the buffer interval to 10 seconds. Option D is correct because reducing the buffer interval from 60 to 10 seconds directly reduces latency without adding significant cost. Option A (increase buffer size) would increase latency. Option B (increase buffer interval) worsens latency. Option C (change to Kinesis Data Streams) adds cost and complexity.

Answer

Replace Firehose with Amazon Kinesis Data Streams

Answer

Increase the buffer size to 10 MB

Answer

Increase the buffer interval to 120 seconds

Question 6

A data engineer is troubleshooting a Lambda function that reads from a Kinesis Data Stream, processes records, and writes to a Kinesis Data Firehose delivery stream. The Firehose delivery stream is configured to deliver data to an S3 bucket. The Lambda function is failing with an access denied error. The IAM policy attached to the Lambda execution role is shown in the exhibit. Which permission is missing?

Accepted Answer

firehose:DescribeDeliveryStream on the Firehose delivery stream. Option B is correct because the Lambda function needs permission to describe the Firehose delivery stream to get the endpoint, and kinesis:DescribeStream is already allowed for the Kinesis stream, but firehose:DescribeDeliveryStream is missing for the Firehose resource. Option A is wrong because Kinesis actions are allowed. Option C is wrong because s3:PutObject is allowed. Option D is wrong because the function is not writing to CloudWatch Logs; the error is about access to Firehose.

Answer

kinesis:PutRecord on the Kinesis stream

Answer

logs:CreateLogGroup and logs:CreateLogStream on the CloudWatch log group

Answer

s3:PutObjectAcl on the S3 bucket

Question 7

A data engineer needs to transform data in an S3 data lake using AWS Glue ETL. The data is in CSV format and needs to be converted to Parquet with partitioning by date. The engineer wants to minimize the number of files written to S3 to improve query performance. Which TWO configuration options should the engineer use? (Select TWO.)

Accepted Answer

Use the coalesce method to reduce the number of output partitions.. Option A (use coalesce) reduces the number of output files by merging partitions. Option C (enable partition pruning) helps queries skip irrelevant partitions, improving performance. Option B is wrong because increasing the number of workers may increase the number of output files. Option D is wrong because disabling compression increases file size. Option E is wrong because using dynamic frame with larger partition size does not directly minimize file count.

Answer

Increase the number of workers in the Glue job to increase parallelism.

Answer

Disable compression in the Parquet output.

Answer

Set the 'groupFiles' option to 'inPartition' in the DynamicFrame writer.

Question 8

A data engineer uses AWS Glue to process data from S3. The Glue job frequently fails with 'Out of Memory' errors. The job reads several large compressed files. What is the MOST effective way to resolve this issue without changing the code?

Accepted Answer

Increase the number of G.1X workers or use G.2X workers. Option C is correct because increasing the number of G.1X workers (DPU) provides more memory per worker. Option A (changing file type) may help but is not directly about memory. Option B (repartition) requires code change. Option D (increasing timeout) does not fix memory.

Answer

Convert the compressed files to uncompressed format before processing

Answer

Repartition the data to fewer partitions

Answer

Increase the job timeout setting

Question 9

A company uses AWS Glue DataBrew for data preparation. The data source is an S3 bucket with millions of small CSV files (each < 1 MB). The DataBrew project takes a long time to load the sample data. What is the most likely cause and solution?

Accepted Answer

The large number of small files causes S3 LIST overhead; concatenate files into larger files. Option B is correct because DataBrew samples data by reading files, and many small files cause high overhead due to S3 LIST and GET requests. Concatenating files into fewer larger files reduces this overhead. Option A (increase DPUs) does not help with the LIST overhead. Option C (use Glue ETL) is a different service. Option D (use Athena) is for querying, not data preparation.

Answer

Use Amazon Athena to query the data instead of DataBrew

Answer

The DataBrew job is under-provisioned; increase the number of DPUs

Answer

Use AWS Glue ETL instead of DataBrew for this volume

Question 10

A company is using Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed from JSON to Parquet format before landing in S3. The transformation logic is simple: convert the JSON schema to Parquet. Which approach meets the requirements with the least operational overhead?

Accepted Answer

Use the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table. Option A is correct because Firehose can natively convert JSON to Parquet using a schema from AWS Glue Data Catalog, without custom code. Option B (Lambda) requires writing and maintaining code. Option C (Kinesis Data Analytics) is overkill. Option D (EC2) adds management overhead.

Answer

Use an AWS Lambda function to transform records to Parquet before sending to Firehose

Answer

Use Amazon Kinesis Data Analytics to convert the stream to Parquet

Answer

Provision an Amazon EMR cluster to convert the data in micro-batches

Question 11

A data engineer needs to schedule an AWS Glue ETL job to run every hour and process new data that arrives in an S3 bucket. The job should only process files that have been added since the last run. Which approach should the engineer use to track which files have been processed?

Accepted Answer

Enable job bookmarks in the Glue ETL job.. Option B is correct because Glue job bookmarks automatically track the last processed files in S3 and only process new data in subsequent runs. Option A is wrong because S3 Event Notifications can trigger the job but do not track processed files; the job would need to handle deduplication. Option C is wrong because a DynamoDB table would require custom code to track state. Option D is wrong because checking last modified timestamp manually is error-prone and not built-in.

Answer

Configure S3 Event Notifications to trigger the Glue job on each new object creation.

Answer

Store the last processed timestamp in a DynamoDB table and query it at the start of the job.

Answer

Use S3 Inventory to list all objects and filter by last modified date in the job.

Question 12

A data engineer is building a data ingestion pipeline using AWS Glue. The source is an Amazon DynamoDB table, and the target is an Amazon S3 data lake in Parquet format. The pipeline must handle large volumes and ensure exactly-once processing. Which THREE features should the engineer use together to achieve this? (Choose THREE.)

Accepted Answer

Configure the Glue job to convert data to Parquet format.. Options A, C, and E are correct. Option A: Glue job bookmarks track processed data to avoid duplicates. Option C: Using the DynamoDB export feature to S3 is efficient for large volumes and provides a consistent snapshot. Option E: Converting to Parquet in the Glue job is a common pattern. Option B is wrong because Kinesis Data Streams is for streaming, not for DynamoDB bulk export. Option D is wrong because S3 Object Lambda is for modifying data on read, not for ingestion.

Answer

Use Amazon Kinesis Data Streams to capture DynamoDB Streams changes.

Answer

Use Amazon S3 Object Lambda to transform data on the fly.

Question 13

A company uses AWS Glue ETL jobs to process data from an S3 data lake. The job reads data in CSV format, transforms it, and writes to Parquet. The job runs daily and takes 2 hours to complete. The data volume is increasing by 20% each month. The engineer wants to reduce the job runtime. Which action is most effective?

Accepted Answer

Partition the input data in S3 by date and use partition pruning in the job. Option B is correct because partitioning data by date (e.g., year/month/day) allows Glue to read only the new partitions incrementally, reducing data scanned. Option A (increasing DPUs) may help but not as much as reducing data volume. Option C (using Spark) is already used by Glue. Option D (compression) is already Parquet, which is compressed.

Answer

Increase the number of DPUs for the Glue job

Answer

Enable compression on the input CSV files

Answer

Switch from Python Shell to Spark ETL

Question 14

A company is using AWS Glue to run ETL jobs that read from Amazon S3 and write to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. Which TWO actions should the data engineer take to resolve this issue? (Choose TWO.)

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Option A is correct because increasing the DPU count provides more memory and processing power. Option B is correct because enabling job metrics helps identify memory bottlenecks. Option C (increasing shuffle threshold) may help but is not a primary solution. Option D (reducing parallelism) could reduce memory but may slow down the job. Option E (changing Redshift) is unrelated to memory in Glue.

Answer

Switch the output to Amazon S3 instead of Redshift

Answer

Reduce the number of partitions in the input data

Answer

Increase the spark.sql.shuffle.partitions parameter

Question 15

A company wants to ingest data from a SaaS application into Amazon S3. The SaaS application supports streaming data via HTTP POST requests. The data volume is approximately 100 MB per hour, and the company needs to store the raw data in S3 for archival and later analysis. Which approach is the most cost-effective and operationally efficient?

Accepted Answer

Use Amazon Kinesis Data Firehose with HTTP endpoint as the source, and configure S3 as the destination.. Option C is correct because Kinesis Data Firehose can directly accept HTTP POST requests via the Kinesis Agent or direct API calls, and it automatically delivers data to S3 with buffering, requiring no servers to manage. Option A is wrong because running an EC2 instance adds operational overhead and cost even if it's small. Option B is wrong because SQS also requires a consumer (e.g., Lambda) to move data to S3, adding complexity. Option D is wrong because API Gateway and Lambda add unnecessary layers and cost for simple ingestion.

Answer

Launch a t3.nano EC2 instance that runs a script to receive HTTP POST requests and write to S3.

Answer

Use Amazon Simple Queue Service (SQS) to queue the HTTP POST data and have an AWS Lambda function read from SQS and write to S3.

Answer

Use Amazon API Gateway to create a REST API that receives the data and triggers an AWS Lambda function to store it in S3.