DEA-C01 Data Ingestion and Transformation Practice Test 21 — 15 Questions

Question 1

A data engineer is designing a real-time streaming pipeline to ingest clickstream data from a website into Amazon S3. The data must be transformed before storage. Which TWO AWS services can be used together to build this pipeline? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. Option A and Option E are correct. Amazon Kinesis Data Streams (A) can ingest streaming data, and Amazon Kinesis Data Firehose (E) can deliver the data to S3 with optional transformation via Lambda. Option B (S3 Transfer Acceleration) is for accelerating uploads to S3, not for streaming. Option C (AWS DMS) is for database migration. Option D (AWS Glue) is for batch ETL, not real-time.

Answer

AWS Glue

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Database Migration Service (DMS)

Question 2

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 3

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 4

A company is ingesting IoT sensor data from thousands of devices using Amazon Kinesis Data Streams. The data is consumed by a Lambda function that transforms and writes to Amazon S3. The company notices that occasionally records are dropped. The data engineer needs to identify the cause and prevent data loss. Which TWO actions should the data engineer take? (Choose TWO.)

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Options A and D are correct. Option A: Increasing shards increases throughput capacity. Option D: Configuring a dead-letter queue (DLQ) for Lambda catches failed records. Option B is wrong because Lambda can process up to 10 MB per batch, but default is not the issue here. Option C is wrong because SQS is not used with Kinesis directly; Lambda polls Kinesis. Option E is wrong because CloudWatch Logs do not prevent data loss.

Answer

Enable CloudWatch Logs on the Kinesis stream to log all records.

Answer

Decrease the Lambda batch size to process records more frequently.

Answer

Add an Amazon SQS queue between Kinesis and Lambda to buffer records.

Question 5

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 6

A financial services company ingests real-time stock trade data using Amazon Kinesis Data Streams with 10 shards. Each shard receives about 500 records per second, each record approximately 1 KB. The data is consumed by a single AWS Lambda function that transforms the data and writes to Amazon S3. The Lambda function is configured with 1024 MB memory and a timeout of 5 minutes. The company notices that the Lambda function is frequently throttled, and data ingestion lags behind. The Lambda function's CloudWatch metrics show that the iterator age is increasing, and the function's concurrency is maxed out at 1000. The data engineer needs to resolve the throttling issue without changing the Lambda function code. What should the data engineer do?

Accepted Answer

Increase the number of shards in the Kinesis data stream to increase parallelism.. Option B is correct. Increasing the number of shards increases the number of Lambda consumers, allowing more parallel processing and reducing the iterator age. Option A is wrong because increasing Lambda memory may not improve throughput if the function is CPU-bound; also, the issue is concurrency, not memory. Option C is wrong because decreasing batch size may increase overhead; the function is already maxing concurrency. Option D is wrong because reducing memory would likely make the function slower.

Answer

Reduce the Lambda function memory to 512 MB to increase concurrency limit.

Answer

Decrease the batch size to 10 records to reduce processing time per invocation.

Answer

Increase the Lambda function memory to 2048 MB to improve processing speed.

Question 7

A retail company uses AWS Glue to process daily sales data from multiple CSV files stored in Amazon S3. The Glue job runs a PySpark script that reads the files, performs joins, and writes the output as Parquet. Recently, the job has been failing with 'Out of Memory' errors. The data volume has grown from 10 GB to 50 GB per day. The Glue job uses 10 DPUs and the standard worker type. The data engineer needs to fix the job without rewriting the script. What should the data engineer do?

Accepted Answer

Increase the number of DPUs for the Glue job to 20.. Option C is correct. Increasing the number of DPUs provides more memory and compute resources, addressing the OOM error. Option A is wrong because changing worker type to G.2X may not be sufficient if the issue is simply memory; but increasing DPUs is a direct solution. Option B is wrong because splitting files does not reduce the memory needed for joins. Option D is wrong because decreasing DPUs would make the problem worse.

Answer

Split the input CSV files into smaller partitions.

Answer

Change the worker type to G.2X to get more memory per worker.

Answer

Decrease the number of DPUs to reduce memory contention.

Question 8

A media company uses Amazon Kinesis Data Firehose to ingest log data from web servers into Amazon S3. The data is then processed by AWS Glue jobs. The company wants to ensure that data is delivered to S3 within 5 minutes of ingestion. Currently, the Firehose delivery stream is configured with a buffer interval of 300 seconds and a buffer size of 5 MB. The log data arrives at a rate of 2 MB per second. The data engineer notices that some log files are delayed by up to 10 minutes. The company cannot change the buffer size due to downstream requirements. What should the data engineer do to meet the 5-minute delivery requirement?

Accepted Answer

Decrease the buffer interval to 120 seconds.. Option A is correct. Decreasing the buffer interval to 120 seconds will cause Firehose to deliver data more frequently, meeting the 5-minute SLA. Option B is wrong because increasing buffer interval would increase delay. Option C is wrong because increasing buffer size would also increase delay. Option D is wrong because compressing data does not reduce buffer interval.

Answer

Increase the buffer interval to 600 seconds to reduce the number of delivery attempts.

Answer

Increase the buffer size to 10 MB to ensure data is delivered in larger chunks.

Answer

Enable GZIP compression on the Firehose stream to reduce data size.

Question 9

A gaming company collects player event data from mobile devices. The data is sent to an Amazon API Gateway endpoint, which triggers an AWS Lambda function that writes the data to an Amazon DynamoDB table. The company wants to also store the data in Amazon S3 for historical analysis. The data volume is about 100 GB per day. The data engineer needs to design a solution to copy data from DynamoDB to S3 with minimal impact on the DynamoDB table. What should the data engineer do?

Accepted Answer

Enable DynamoDB Streams on the table and configure a Lambda function to write changes to S3.. Option A is correct. Using DynamoDB Streams with a Lambda function that writes to S3 is a common pattern for real-time replication with minimal impact. Option B is wrong because Export to S3 is a one-time or scheduled export, not continuous. Option C is wrong because using Scan would consume read capacity and impact performance. Option D is wrong because adding a secondary index does not help with exporting data.

Answer

Create a global secondary index on the table and export the index to S3.

Answer

Use AWS Glue to scan the DynamoDB table and write results to S3 every hour.

Answer

Use the DynamoDB Export to S3 feature to export the entire table daily.

Question 10

A healthcare company processes patient records in near-real-time using Amazon Kinesis Data Streams. Each record contains sensitive personal health information (PHI). The data must be encrypted at rest and in transit. The company also needs to audit access to the data. The data engineer is designing the ingestion pipeline. Which combination of services and configurations meets these requirements?

Accepted Answer

Use Kinesis Data Streams with SSE-KMS and TLS, and enable CloudTrail for data events.. Option C is correct. Kinesis Data Streams supports server-side encryption (SSE) using AWS KMS for at-rest encryption, and TLS for in-transit. CloudTrail can log Kinesis API calls for auditing. Option A lacks encryption at rest. Option B lacks auditing. Option D is wrong because S3 does not replace Kinesis for streaming.

Answer

Use Kinesis Data Firehose to deliver data to S3 with SSE-S3, and enable CloudTrail for S3.

Answer

Use Kinesis Data Streams with TLS and enable CloudTrail for auditing. Do not enable SSE.

Answer

Use Kinesis Data Streams with SSE-KMS and TLS. Do not enable any auditing.

Question 11

A logistics company uses AWS Glue to process GPS data from delivery trucks. The data is stored in Amazon S3 as JSON files. The Glue job reads the JSON files, converts them to Parquet, and writes them back to S3. The company notices that the Glue job takes too long to complete. The data engineer wants to improve the job's performance without changing the code. What should the data engineer do?

Accepted Answer

Change the worker type to G.2X.. Option D is correct. Using a G.2X worker type provides more memory and CPU per worker, improving performance. Option A is wrong because increasing DPUs may help but is less efficient than using larger workers for memory-intensive tasks. Option B is wrong because decreasing DPUs would worsen performance. Option C is wrong because using G.1X is the default; upgrading to G.2X is better.

Answer

Increase the number of DPUs to 20.

Answer

Change the worker type to G.1X.

Answer

Decrease the number of DPUs to 5 to reduce overhead.

Question 12

A social media company ingests user activity data from multiple sources using Amazon Kinesis Data Firehose. The data is delivered to Amazon S3 in near-real-time. The company wants to transform the data by adding a timestamp and masking email addresses before storing it in S3. The transformation should be applied to all records. What is the most cost-effective way to implement this transformation?

Accepted Answer

Configure the Firehose delivery stream to invoke a Lambda function for data transformation.. Option A is correct. Kinesis Data Firehose can invoke a Lambda function to transform data on the fly. This is cost-effective because it runs only when data is flowing. Option B is wrong because Glue jobs are batch-oriented and add latency. Option C is wrong because S3 Events with Lambda adds complexity and cost. Option D is wrong because Athena is for querying, not transforming.

Answer

Use Amazon Athena to run a CTAS query that transforms the data and writes to a new location.

Answer

Use AWS Glue to schedule a batch job every 5 minutes to transform the data.

Answer

Use Amazon S3 Events to trigger a Lambda function whenever a new object is created.

Question 13

An e-commerce company uses AWS Glue to process clickstream data from its website. The data is stored in Amazon S3 in partitioned Parquet format by date and hour. A recent increase in traffic has caused the Glue job to fail with 'Java heap space' errors. The job runs with 10 DPUs and uses Spark's default configurations. The data engineer needs to resolve the memory issue without modifying the ETL script. What should the data engineer do?

Accepted Answer

Increase the Spark configuration 'spark.sql.shuffle.partitions' to 500.. Option B is correct. Increasing Spark's shuffle partitions reduces the amount of data handled per partition, preventing heap space errors. Option A is wrong because increasing DPUs may not directly address the heap space issue if the problem is within a single executor. Option C is wrong because reducing partitions may increase memory pressure. Option D is wrong because changing worker type to G.1X may not help if the issue is shuffle-related.

Answer

Decrease the Spark configuration 'spark.sql.shuffle.partitions' to 50.

Answer

Change the worker type to G.1X.

Answer

Increase the number of DPUs to 20.

Question 14

A data engineer needs to ingest data from an on-premises Apache Kafka cluster into Amazon S3. The data engineer wants to minimize operational overhead and avoid managing any servers. Which AWS service should the data engineer use?

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Option C is correct. Amazon MSK is a fully managed Kafka service that can be used to ingest data from on-premises Kafka via mirroring or replication. Option A is wrong because Kinesis Data Analytics is for analyzing streaming data, not for ingestion. Option B is wrong because Kinesis Data Streams is a different streaming service, not compatible with Kafka without custom connectors. Option D is wrong because Glue is for ETL, not for streaming ingestion from Kafka.

Answer

AWS Glue

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Kinesis Data Streams

Question 15

A data engineer needs to ingest streaming data from an e-commerce application into Amazon S3 for near-real-time analytics. The solution must handle variable throughput and allow reprocessing of failed records. Which TWO AWS services should the engineer use? (Choose two.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams ingests streaming data reliably, and Amazon Kinesis Data Firehose delivers it to S3 with retry logic. Option A (Amazon SQS) is for message queues, not streaming ingestion. Option B (Amazon RDS) is a database. Option D (AWS Lambda) can process records but is not the primary ingestion service for high-throughput streaming. Option E (Amazon DynamoDB Streams) is for CDC from DynamoDB, not general streaming.

Answer

Amazon SQS

Answer

Amazon DynamoDB Streams

Answer

Amazon RDS