DEA-C01 Practice Exam 55 — 20 Questions

Question 1

A company has a large volume of CSV files in S3 that need to be transformed into Parquet using AWS Glue. The files are partitioned by date. The engineer wants to minimize costs by processing only new files each day. Which approach should be used?

Accepted Answer

Enable job bookmarks in the Glue job.. Option C is correct because using a Job Bookmark in Glue tracks processed data and skips already processed files, processing only new ones. Option A is wrong because it would reprocess all files. Option B is wrong because partitioning alone does not prevent reprocessing. Option D is wrong because an SQS event can trigger a job, but without bookmarks, it may still reprocess.

Answer

Use S3 partition discovery to automatically read new partitions.

Answer

Schedule the Glue job to run daily and process all files.

Answer

Use S3 Event Notifications to trigger the Glue job on each new file.

Question 2

A data engineer runs an AWS Glue job that reads from a JDBC connection to a PostgreSQL database. The job fails with a 'Connection timed out' error. The Glue job runs in a VPC with the appropriate security group. What is the most likely cause?

Accepted Answer

The network ACL associated with the Glue job's subnet is blocking outbound traffic.. Option C is correct because a network ACL can block outbound traffic from the VPC to the database. Option A is wrong because the error is network, not authentication. Option B is wrong because the security group allows inbound, but need outbound. Option D is wrong because it's a network issue, not schema.

Answer

The Glue job does not have permission to access the database.

Answer

The security group does not allow inbound traffic from the Glue job.

Answer

The database credentials are incorrect.

Question 3

A data engineering team needs to ingest streaming data from an application into Amazon S3 for analytics. The data volume is moderate and the team wants the lowest operational overhead. Which AWS service should they use?

Accepted Answer

Amazon Kinesis Data Firehose. Option B is correct because Amazon Kinesis Data Firehose is a fully managed service for loading streaming data into S3 with no code required. Option A is wrong because Amazon Kinesis Data Streams requires custom consumers. Option C is wrong because AWS Glue is for batch ETL, not real-time. Option D is wrong because Amazon SQS is a message queue, not optimized for streaming analytics destinations.

Answer

Amazon SQS

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Question 4

A data engineering team is designing a near-real-time data ingestion pipeline for IoT sensor data. The data must be processed and stored in Amazon S3, with transformations applied before storage. The team needs to handle potential duplicates and ensure exactly-once processing semantics. Which TWO AWS services should be used together? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Options B and C are correct. Kinesis Data Streams provides ordered data and can integrate with Kinesis Data Analytics for Flink, which supports exactly-once semantics. Option A (Firehose) provides at-least-once delivery. Option D (SQS) is for decoupling but does not provide exactly-once. Option E (DMS) is for database replication.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Database Migration Service (DMS)

Question 5

A company uses Amazon Kinesis Data Analytics to process real-time data. The application needs to aggregate data over a 10-minute window. The team notices that late-arriving events are being dropped. Which configuration should they adjust?

Accepted Answer

Set the allowed_lateness parameter in the application's windowed aggregation.. Option C is correct because Kinesis Data Analytics allows you to set a lateness tolerance (allowed_lateness) for streaming aggregations. Option A is wrong because RecordColumn is for schema. Option B is wrong because ShardCount affects parallelism, not late events. Option D is wrong because the application uses Kinesis Data Analytics, not Firehose.

Answer

Configure a Kinesis Firehose delivery stream to buffer the late events.

Answer

Increase the shard count of the source Kinesis stream.

Answer

Increase the RecordColumn count in the input stream mapping.

Question 6

A company is streaming IoT sensor data from thousands of devices into Amazon Kinesis Data Firehose. The data is then delivered to Amazon S3 for long-term storage. Occasionally, some records fail to be delivered to S3. The company must capture and analyze these failed records. Which TWO actions should be taken? (Choose two.)

Accepted Answer

Configure an AWS Lambda function as a pre-processing step to catch and log failed records.. A is correct because enabling Kinesis Data Firehose error logging to CloudWatch allows monitoring of delivery failures. C is correct because configuring a Lambda function as a data transformation and error handler can process and log failed records. B is wrong because S3 is the destination, not the source for reprocessing. D is wrong because Kinesis Data Streams is a different service and does not directly capture Firehose failures. E is wrong because Kinesis Data Analytics is for real-time analytics, not for handling delivery failures.

Answer

Use Amazon Kinesis Data Analytics to analyze the failed records in real time.

Answer

Send failed records to an Amazon Kinesis Data Stream for reprocessing.

Answer

Set up an S3 event notification to trigger a Lambda function to reprocess failed records.

Question 7

A data pipeline uses AWS Glue to read CSV files from an S3 bucket, transform them, and write Parquet back to S3. The pipeline runs daily and processes about 500 GB per run. The team wants to reduce costs without increasing runtime. Which approach is most effective?

Accepted Answer

Optimize the Glue script to select only required columns and filter rows early.. Option D is correct because using column pruning and predicate pushdown reduces the amount of data scanned, lowering costs without affecting runtime. Option A is wrong because increasing DPUs may increase cost and runtime unpredictably. Option B is wrong because converting to Parquet upfront would add an extra processing step and cost. Option C is wrong because job bookmarks track processed data but do not reduce cost per run.

Answer

Pre-convert the CSV files to Parquet in S3 using a separate process.

Answer

Enable job bookmarks to skip already processed data.

Answer

Increase the number of DPUs for the Glue job to improve parallelism.

Question 8

A company uses Amazon Kinesis Data Firehose to ingest data into an S3 bucket. The data is in JSON format and the team wants to convert it to Parquet before storage. Which TWO configurations are required?

Accepted Answer

Create a Glue table with the schema of the data.. Options A and B are correct: Firehose can convert to Parquet if you specify a data format conversion and a Glue table (schema). Option C (Kinesis Data Analytics) is not needed. Option D (Athena) is for querying. Option E (Lambda) can be used but is not required.

Answer

Use Kinesis Data Analytics to transform data to Parquet.

Answer

Configure a Lambda function to convert data on the fly.

Answer

Set up an Athena table to read the data.

Question 9

A company wants to move data from an Amazon RDS for MySQL database to Amazon Redshift for analytics. The data needs to be refreshed daily. Which AWS service is best suited for this?

Accepted Answer

AWS Glue. Option C is correct because AWS Glue can connect to RDS and Redshift, and schedule jobs. Option A (DMS) is for ongoing replication, not necessarily for daily batch. Option B (Athena) queries S3. Option D (EMR) is for big data processing but overkill for simple transfer.

Answer

AWS Database Migration Service (DMS)

Answer

Amazon EMR

Answer

Amazon Athena

Question 10

A company wants to ingest data from thousands of IoT devices into AWS for real-time analytics. The data is in JSON format and each device sends about 1 KB every second. Which service should be used as the primary ingestion point?

Accepted Answer

Amazon Kinesis Data Streams. Option B is correct because Kinesis Data Streams can handle high throughput real-time data from many sources. Option A (SQS) is for message queuing, not streaming analytics. Option C (Firehose) could be used but lacks the ability to have multiple consumers for real-time processing. Option D (IoT Core) is specifically for IoT but not as general-purpose.

Answer

AWS IoT Core

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS

Question 11

A data pipeline ingests streaming data from thousands of IoT devices into Kinesis Data Streams. The data must be transformed using a simple field mapping before being stored in S3. Which service should be used to perform the transformation with minimal operational overhead?

Accepted Answer

Kinesis Data Firehose with a Lambda transformation. Option C is correct because Kinesis Data Firehose can perform simple transformations using Lambda functions before delivering data to S3. Option A is wrong because Glue ETL is better for complex batch transformations. Option B is wrong because Kinesis Data Analytics is for real-time analytics, not simple field mapping. Option D is wrong because Lambda alone would require managing the delivery to S3.

Answer

AWS Lambda function invoked by the Kinesis stream

Answer

AWS Glue ETL job

Answer

Kinesis Data Analytics

Question 12

A company needs to ingest data from an external FTP server into AWS S3. The FTP server is not accessible from the internet. Which AWS service should be used to securely transfer the data?

Accepted Answer

AWS Transfer Family with SFTP endpoint in a VPC. Option B is correct because AWS Transfer Family supports SFTP and can be used with a VPC endpoint to transfer data from an FTP server in a private network to S3. Option A is wrong because Snowball Edge is for large offline data transfers, not regular FTP transfers. Option C is wrong because DataSync is for moving data between on-premises storage and AWS, but it requires network access. Option D is wrong because Kinesis Data Firehose is for streaming data, not FTP transfers.

Answer

Kinesis Data Firehose

Answer

AWS DataSync

Answer

AWS Snowball Edge

Question 13

A company uses Kinesis Data Firehose with a Lambda function for data transformation. The transformation is failing intermittently due to Lambda timeouts. The maximum record size is 1 MB. What is the most cost-effective way to reduce failures without losing data?

Accepted Answer

Increase the Lambda function timeout and memory allocation. Option A is correct because increasing the Lambda timeout and memory reduces timeouts while remaining cost-effective. Option B is wrong because sending to a dead-letter queue (DLQ) loses data. Option C is wrong because decreasing batch size may increase costs. Option D is wrong because using Kinesis Data Analytics adds unnecessary complexity and cost.

Answer

Use Kinesis Data Analytics to pre-process data before Firehose

Answer

Decrease the Firehose batch size to reduce the number of records per invocation

Answer

Configure the Firehose delivery stream to send failed records to an S3 dead-letter bucket

Question 14

A company ingests streaming data from social media APIs into Kinesis Data Streams. Each record is approximately 5 KB. The data must be enriched with geolocation information from a DynamoDB table before being stored in S3. The enrichment process takes about 200 ms per record. Which architecture minimizes latency and cost?

Accepted Answer

Use Kinesis Data Firehose with a Lambda function that queries DynamoDB. Option D is correct because using Kinesis Data Firehose with a Lambda function for enrichment is efficient for moderate-size records. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not enrichment. Option B is wrong because EC2 adds significant operational overhead. Option C is wrong because Glue ETL is for batch processing, not streaming.

Answer

Use an EC2 instance running a custom application to consume from Kinesis, enrich, and write to S3

Answer

Use AWS Glue ETL jobs running continuously on the stream

Answer

Use Kinesis Data Analytics to perform enrichment with SQL

Question 15

A data engineering team is designing a batch processing workflow using AWS Glue. The job reads from an S3 bucket, transforms data, and writes to another S3 bucket. The job runs daily and processes new data incrementally. Which THREE features should they use to optimize performance and cost?

Accepted Answer

Enable Glue job autoscaling.. Options B, C, and D are correct: job bookmarks for incremental processing, autoscaling for dynamic resource allocation, and predicate pushdown for reducing data scanned. Option A (increasing DPUs manually) is not optimal. Option E (converting to Parquet) may help but is not a Glue feature.

Answer

Convert all input data to Parquet format before processing.

Answer

Manually increase the number of DPUs for each run.

Question 16

A company uses S3 as a data lake. They want to ingest on-premises relational database data daily with full-load snapshots. The data volume is 500 GB per day. The database is accessible over the internet. Which service should they use for this ingestion?

Accepted Answer

AWS Database Migration Service (DMS). Option D is correct because AWS Database Migration Service (DMS) can perform full-load migrations from on-premises databases to S3. Option A is wrong because Kinesis Data Firehose is for streaming data, not database snapshots. Option B is wrong because Glue ETL can read from JDBC but is not optimized for large full-load snapshots. Option C is wrong because Transfer Family is for file transfers over SFTP, not database connections.

Answer

Kinesis Data Firehose

Answer

AWS Glue ETL job reading from JDBC

Answer

AWS Transfer Family

Question 17

A company wants to ingest data from an on-premises SQL Server database into Amazon Redshift. They need to transform the data during ingestion, such as masking PII columns. Which approach meets these requirements with minimal operational overhead?

Accepted Answer

Use AWS Database Migration Service (DMS) with transformation rules. Option C is correct because AWS DMS can perform transformation tasks, including data masking, during migration and can load directly into Redshift. Option A is wrong because Glue ETL adds an extra step. Option B is wrong because Kinesis Data Firehose is for streaming data. Option D is wrong because using EC2 adds operational overhead.

Answer

Use AWS Glue ETL jobs to extract data from SQL Server, transform it, and load into Redshift

Answer

Use a custom application on EC2 to extract, transform, and load

Answer

Use Kinesis Data Firehose to stream data from SQL Server to Redshift

Question 18

A company uses Kinesis Data Firehose to deliver streaming data to S3. They need to transform the data by adding a timestamp and removing sensitive fields. Which TWO approaches can achieve this?

Accepted Answer

Use AWS Glue ETL to process data after delivery to S3. Option A and Option D are correct because Lambda transformation can modify records, and Glue ETL can process data from Firehose's S3 destination. Option B is wrong because Kinesis Data Analytics is for analytics, not transformation. Option C is wrong because S3 Select is for retrieving subsets of data, not transformation. Option E is wrong because Redshift Spectrum is for querying data in S3.

Answer

Use Kinesis Data Analytics to transform the stream

Answer

Use S3 Select to transform data at rest

Answer

Use Amazon Redshift Spectrum to transform data

Question 19

A company uses AWS Glue to catalog and transform data in Amazon S3. The Glue ETL jobs are failing intermittently with 'ThrottlingException' errors. Which THREE actions can help mitigate this issue? (Select THREE.)

Accepted Answer

Implement exponential backoff and retry in the Glue job code.. Options A, C, and D are correct. Implementing retry logic with exponential backoff handles transient throttling. Increasing the service quota for Glue API calls if the limit is reached. Using Glue job bookmarking reduces reprocessing, thus lowering API calls. Option B (increasing DPUs) addresses resource issues but not API throttling. Option E (changing to Spark SQL) does not affect API calls.

Answer

Increase the number of DPUs for the Glue job.

Answer

Switch from PySpark to Spark SQL.

Question 20

A data engineer is ingesting data from an on-premises database to Amazon S3 using AWS DataSync. The data transfer is scheduled to run daily at midnight. The engineer notices that the transfer takes longer than expected and sometimes does not complete before the next scheduled task. What should the engineer do to ensure the transfer completes within the window?

Accepted Answer

Increase the bandwidth limit in the DataSync task settings.. Option C is correct because increasing the bandwidth limit allows DataSync to use more network capacity, speeding up the transfer. Option A is wrong because reducing the schedule frequency does not solve the completion time issue. Option B is wrong because changing to S3 Transfer Acceleration adds cost but may not help if the bottleneck is on-premises bandwidth. Option D is wrong because decreasing the limit would make it slower.

Answer

Decrease the bandwidth limit to reduce network congestion.

Answer

Increase the schedule frequency to every 12 hours.

Answer

Use S3 Transfer Acceleration instead of DataSync.