DEA-C01 Practice Test 40 — 15 Questions

Question 1

A data engineer is designing a streaming pipeline that ingests data from an Amazon Kinesis Data Stream (with 5 shards) into Amazon S3. The data must be transformed using a complex stateful operation that cannot be done in a Lambda function (limited to 15 minutes). The engineer needs a solution that can maintain state across multiple records. Which service should be used?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Option A is correct because Kinesis Data Analytics for Apache Flink supports stateful processing with checkpointing. Option B (Firehose) is stateless. Option C (Glue Streaming) is in beta and less mature. Option D (EMR) requires cluster management.

Answer

Amazon EMR running Spark Structured Streaming

Answer

Amazon Kinesis Data Firehose with Lambda transformation

Answer

AWS Glue streaming ETL job

Question 2

A company needs to ingest data from multiple SaaS applications (Salesforce, Marketo) and load it into Amazon Redshift. The data must be transformed before loading. Which AWS service should be used to build the ingestion pipelines?

Accepted Answer

AWS Glue (crawlers and ETL jobs). Option A is correct because AWS Glue can connect to various sources via JDBC and perform ETL transformations. Option B (AppFlow) is for ingesting SaaS data but does not have built-in transformation capabilities; it can only perform simple field mappings. Option C (DMS) is for database migrations, not SaaS. Option D (Data Pipeline) is older and less flexible.

Answer

AWS Database Migration Service (DMS)

Answer

AWS Data Pipeline

Answer

Amazon AppFlow

Question 3

A data engineer is designing a data ingestion pipeline for clickstream data from a mobile app. The data volume varies, with occasional spikes up to 10 MB/s. The pipeline must persist the raw data in Amazon S3 and make it available for near-real-time analytics via Amazon Athena. Which combination of services minimizes cost and operational overhead?

Accepted Answer

Amazon Kinesis Data Firehose with direct delivery to Amazon S3, then Amazon Athena. Option C is correct because Firehose can buffer and deliver data to S3 with minimal configuration, and Athena can query directly from S3. Option A (Kinesis Data Streams + Lambda) incurs higher costs and management overhead. Option B (SQS + EC2) requires managing EC2 instances and auto-scaling. Option D (Kinesis Data Streams + Kinesis Data Analytics) adds complexity and cost.

Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics, then Amazon S3

Answer

Amazon SQS with an Auto Scaling group of EC2 instances writing to Amazon S3

Answer

Amazon Kinesis Data Streams with AWS Lambda for transformation, then Amazon S3

Question 4

Refer to the exhibit. A data engineer runs the AWS CLI command to describe a Glue job. The job is expected to process new data incrementally using job bookmarks. However, the job reprocesses all data every time it runs. What is the MOST likely reason?

Accepted Answer

The ETL script does not use the 'transformation_ctx' parameter in its DynamicFrame transformations.. Option B is correct because Glue job bookmarks are only supported when using Spark (glueetl) with the correct script type. The exhibit shows 'Command.Name' is 'glueetl', which is correct. But the issue might be that the script does not use the 'transformation_ctx' parameter properly. However, the exhibit shows '--job-bookmark-option' set to 'job-bookmark-enable', which is correct. Option B is wrong because the command is glueetl. Option A is wrong because bookmarks are enabled. Option C is wrong because MaxRetries does not affect bookmarks. Option D is correct because job bookmarks require the Glue job to read from a source that supports bookmarks (like S3) and the script must use the 'transformation_ctx' to enable bookmarks. But the exhibit doesn't show the script. However, a common reason is that the job's source is not S3 or that the script does not use the 'transformation_ctx' properly. Let me re-express: Option D is the most likely because the script may not be using the 'transformation_ctx' in the DynamicFrame. I'll set D as correct.

Answer

The job bookmark option is set to 'job-bookmark-enable' but should be 'job-bookmark-disable'.

Answer

The job's MaxRetries is set to 0, which disables bookmarks.

Answer

The Glue job's command name is 'glueetl', which does not support job bookmarks.

Question 5

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is compressed with GZIP and partitioned by year, month, day, and hour. The delivery stream is configured to buffer up to 5 MB or 60 seconds. Some records are missing from S3. What is the most likely cause?

Accepted Answer

The Lambda transformation function timed out and records were skipped. Option A is correct because Kinesis Data Firehose transformation Lambda functions have a 5-minute timeout; if the transformation takes longer, records are dropped. Option B is wrong because GZIP compression is supported. Option C is wrong because partitioning does not cause data loss. Option D is wrong because S3 permissions would cause delivery failures, not silent missing records.

Answer

The S3 bucket does not have sufficient permissions

Answer

The data compression format is incompatible with S3

Answer

The partition key configuration is incorrect

Question 6

A data engineer is designing a data ingestion pipeline for clickstream data. The data arrives in batches of 10-50 MB every 5 seconds. The engineer needs to buffer the data, perform simple transformations (e.g., add timestamp, remove PII), and land it in S3 within 10 minutes. Which TWO services should be combined? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. Option A (Kinesis Data Firehose) can buffer and invoke Lambda for transformation, delivering to S3. Option E (Lambda) can perform the transformation. Option B (SQS) is for decoupling, not streaming to S3. Option C (Kinesis Data Streams) requires custom consumer. Option D (Glue) is batch-oriented.

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Glue ETL

Answer

Amazon Kinesis Data Streams

Question 7

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The Firehose delivery stream has a buffer size of 64 MB and a buffer interval of 300 seconds. The data volume is 1 GB per minute, and the average record size is 1 KB. The data must be delivered to S3 within 5 minutes of ingestion. The engineer notices that some files are being delivered after 10 minutes. What is the most likely cause?

Accepted Answer

The S3 bucket is throttling PUT requests due to high throughput. Option A is correct because with 1 KB records and 64 MB buffer, it takes ~64,000 records to fill the buffer. At 1 GB/min (~1,000,000 records/min), the buffer fills in ~3.84 seconds, so the 300-second interval is not the bottleneck. However, if the S3 bucket has a PUT request rate limit, Firehose may retry and cause delays. Option B is incorrect because 64 MB is default, not too small. Option C is incorrect because 300 seconds is actually long, but data volume is high enough to trigger delivery sooner. Option D is incorrect because compression would reduce size, not cause delays.

Answer

The buffer size of 64 MB is too small for the data volume

Answer

The data is not compressed, causing larger file sizes

Answer

The buffer interval of 300 seconds is too long

Question 8

A data engineer is designing a data ingestion pipeline for real-time financial transactions. The pipeline must ensure exactly-once processing semantics and must handle duplicate records that may occur due to retries. Which combination of AWS services can achieve exactly-once processing?

Accepted Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics for Apache Flink. Option A is correct because Kinesis Data Streams provides sequence numbers, and Kinesis Data Analytics can use Flink's exactly-once semantics with idempotent sinks. Option B (Firehose) provides at-least-once. Option C (SQS) provides at-least-once. Option D (MSK) can achieve exactly-once but requires more configuration and is not as straightforward.

Answer

Amazon SQS with AWS Lambda

Answer

Amazon MSK with AWS Lambda

Answer

Amazon Kinesis Data Firehose with AWS Lambda

Question 9

A company needs to ingest data from multiple on-premises databases into Amazon S3 for analytics. The databases include Oracle, MySQL, and PostgreSQL. The data must be continuously replicated with minimal latency. Which AWS service should be used?

Accepted Answer

AWS Database Migration Service (AWS DMS). Option B is correct because AWS DMS supports heterogeneous database migrations and continuous replication to S3. Option A is wrong because Glue is batch-oriented. Option C is wrong because Kinesis Data Streams is for streaming data, not database replication. Option D is wrong because Snowball is for large offline transfers.

Answer

Amazon Kinesis Data Streams

Answer

AWS Snowball

Answer

AWS Glue

Question 10

A data engineer needs to load data from an on-premises Oracle database to Amazon S3 daily. The table is 500 GB and grows by 50 MB per day. The load must capture only new and changed rows since the last run. Which solution is MOST cost-effective and requires the least maintenance?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication (CDC). Option D is correct because AWS DMS with change data capture (CDC) can continuously replicate changes with minimal setup. Option A (full export) is wasteful. Option B (Glue with JDBC) does not natively support CDC. Option C (custom script) increases maintenance.

Answer

Write a custom Python script on EC2 to query the Oracle redo logs and upload to S3

Answer

Export the entire table to CSV daily using a script and upload to S3

Answer

Use AWS Glue ETL job with a JDBC connection and a timestamp filter

Question 11

A company is streaming clickstream data from a website into Amazon Kinesis Data Streams. The data must be transformed in near real-time and stored in Amazon S3 for analytics. Which AWS service should be used to transform the data as it is ingested?

Accepted Answer

Amazon Kinesis Data Analytics. Option B is correct because Amazon Kinesis Data Analytics can process and transform streaming data in real-time using SQL or Apache Flink. Option A (AWS Glue) is a batch ETL service, not for real-time streams. Option C (Amazon EMR) is for big data processing but requires more setup. Option D (AWS Lambda) can process Kinesis records but is less efficient for complex transformations on high-throughput streams.

Answer

AWS Lambda (streaming function)

Answer

Amazon EMR (Spark Streaming)

Answer

AWS Glue (ETL jobs)

Question 12

A company runs a real-time analytics platform using Amazon Kinesis Data Streams. The data is consumed by multiple consumers: one for real-time dashboard (using Lambda) and one for long-term storage (using Firehose to S3). The Kinesis stream has 10 shards. Each record is 1 KB, and the total incoming data rate is 5 MB/s. The Lambda consumer is falling behind and processing latency exceeds 10 seconds. Which TWO actions should be taken to resolve the issue?

Accepted Answer

Increase the number of shards to 20. Options A and D are correct. Increasing the number of shards to 20 doubles the throughput capacity, allowing Lambda to process more records per second. Using enhanced fan-out eliminates contention between consumers, giving each consumer dedicated read throughput. Option B (increase Lambda memory) may help but limited by shard throughput. Option C (decrease batch size) would increase number of invocations, possibly worsening latency. Option E (use KCL) is already used by Lambda.

Answer

Increase the Lambda function's memory allocation

Answer

Switch to using Kinesis Client Library (KCL) instead of Lambda

Answer

Decrease the batch size in the Lambda event source mapping

Question 13

A company needs to ingest data from multiple SaaS sources (e.g., Salesforce, Marketo) into Amazon S3 for analytics. Which AWS service is designed for this purpose?

Accepted Answer

Amazon AppFlow. Option B is correct because AppFlow is a fully managed service for securely transferring data from SaaS applications to AWS. Option A (Glue) can connect to JDBC sources but not directly to many SaaS APIs. Option C (DataSync) is for file-based transfers. Option D (Transfer Family) is for SFTP.

Answer

AWS Transfer Family

Answer

AWS Glue

Answer

AWS DataSync

Question 14

A data engineer needs to ingest streaming data from thousands of IoT devices into AWS for near-real-time analytics. The data volume varies significantly and can spike unpredictably. The engineer wants to minimize operational overhead and ensure that data is durably stored as soon as it arrives. Which AWS service combination should the engineer use?

Accepted Answer

Use Amazon Kinesis Data Streams to ingest the data and AWS Lambda to process records in real-time with automatic scaling.. Option D is correct because Kinesis Data Streams provides durable, scalable ingestion for streaming data, and Lambda can process records in near-real-time with automatic scaling. Option A is wrong because S3 Transfer Acceleration is for accelerating uploads to S3, not for streaming ingestion. Option B is wrong because Kinesis Data Firehose is designed for loading streaming data into destinations like S3 but does not offer sub-second latency and has buffering delays. Option C is wrong because SQS is a message queue that decouples producers and consumers but does not natively support streaming data partitioning or replay.

Answer

Use Amazon S3 Transfer Acceleration with S3 Event Notifications to trigger AWS Lambda for processing.

Answer

Use Amazon Kinesis Data Firehose to ingest data into Amazon S3 and use AWS Lambda to transform data during delivery.

Answer

Use Amazon Simple Queue Service (SQS) to buffer the streaming data and configure an Auto Scaling group of EC2 instances to poll and process the data.

Question 15

A data engineer is troubleshooting a Kinesis Data Streams consumer application that is falling behind. The stream has 10 shards and is receiving 5 MB/s of data. The consumer uses the Kinesis Client Library (KCL) with a single worker. The worker is processing all 10 shards but is experiencing high latency and checkpointing delays. Which THREE actions should the engineer take to improve consumer performance? (Select THREE.)

Accepted Answer

Increase the number of KCL workers to match the number of shards.. Option A (increase number of workers) allows shard distribution across multiple workers, improving parallelism. Option D (increase KCL maxRecords per call) reduces the number of API calls, improving throughput. Option E (enable enhanced fan-out) dedicates throughput to the consumer, reducing read throttling. Option B is wrong because increasing shards would increase the load on the consumer. Option C is wrong because reducing the checkpoint interval would cause more frequent checkpointing, potentially increasing delays.

Answer

Decrease the checkpoint interval to reduce checkpointing overhead.

Answer

Increase the number of shards in the stream.