DEA-C01 Practice Test 31 — 15 Questions

Question 1

A company uses Amazon Kinesis Data Analytics (now Managed Service for Apache Flink) to run a Flink application on streaming data. The application fails with 'OutOfMemoryError: Java heap space'. The data volume is 10 MB/s. What is the most likely cause and solution?

Accepted Answer

The application's Parallelism is too low; increase the number of Parallelism and KPUs.. Insufficient Parallelism or KPU allocation leads to OOM. Option A is correct. Option B is wrong because checkpointing actually helps. Option C is wrong because Flink can handle 10 MB/s with proper resources. Option D is wrong because data format does not cause OOM.

Answer

The data contains records larger than 1 MB; split records into smaller chunks.

Answer

Checkpointing is enabled too frequently; reduce checkpoint interval.

Answer

The Flink application is not suitable for 10 MB/s throughput; use Kinesis Data Firehose instead.

Question 2

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time and then stored in Amazon S3. Which AWS service should be used to perform the transformation?

Accepted Answer

Amazon Kinesis Data Analytics. Option A is correct because Amazon Kinesis Data Analytics can process and transform streaming data in real-time using SQL or Apache Flink. Option B is wrong because AWS Glue is a batch ETL service, not real-time. Option C is wrong because Amazon EMR is for big data processing, not real-time streaming transformation. Option D is wrong because Amazon Athena is an interactive query service, not for real-time transformation.

Answer

AWS Glue

Answer

Amazon EMR

Answer

Amazon Athena

Question 3

A data engineer needs to transform data in Amazon S3 using AWS Glue. The job must handle schema evolution and partition pruning. Which THREE features should be used?

Accepted Answer

AWS Glue Data Catalog. Options A, B, and D are correct. Glue crawlers update the schema, partitions enable pruning, and the Data Catalog stores metadata. Option C is wrong because Bookmarks are for state tracking, not schema evolution. Option E is wrong because FindMatches is for deduplication.

Answer

AWS Glue job bookmarks

Answer

AWS Glue FindMatches transform

Question 4

A data engineer is ingesting XML data from an external API into Amazon S3. The engineer needs to transform the XML to JSON using AWS Glue. The XML structure is deeply nested. Which Apache Spark method should be used in the Glue ETL script?

Accepted Answer

Use spark.read.format('xml') with Databricks XML library. Spark's Databricks XML library (spark-xml) can parse XML and convert to DataFrame; then write as JSON. The 'com.databricks.spark.xml' format is the standard. AWS Glue's built-in 'xml' option is limited; 'from_xml' is not a Spark method; 'XmlInputFormat' is Hadoop MR.

Answer

Use the built-in AWS Glue 'xml' data source

Answer

Use Hadoop's XmlInputFormat

Answer

Use the Spark SQL function from_xml()

Question 5

A company wants to migrate on-premises data to Amazon S3 using AWS DataSync. The data is 10 TB and the network bandwidth is 1 Gbps. The migration must be completed within 48 hours. What should the data engineer do to meet the deadline?

Accepted Answer

Use AWS DataSync with multiple agents and enable data compression. Option D (Use AWS DataSync with multiple agents and enable data compression) is correct because it maximizes throughput. Option A (Use Snowball Edge) is not necessary for 10 TB with 1 Gbps; it would take about 22 hours theoretically, but compression and multiple agents can help. Option B (Increase bandwidth) is not always feasible. Option C (Use S3 Transfer Acceleration) improves upload speed but not as much as multiple agents and compression.

Answer

Use S3 Transfer Acceleration to speed up the transfer

Answer

Use AWS Snowball Edge to transfer the data physically

Answer

Request a bandwidth increase from the ISP

Question 6

A data engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 in near real-time. The data must be processed with minimal latency and stored in a columnar format for analytics. Which service should the engineer use to ingest the data?

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, and it can convert data to columnar formats like Parquet and ORC. Option A (Kinesis Data Streams) requires custom consumers. Option C (Kinesis Data Analytics) processes data but does not load to S3 directly. Option D (SQS) is a message queue, not designed for streaming ingestion.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams with a Lambda consumer

Question 7

A company uses AWS Glue to run ETL jobs daily. The data engineer wants to reduce costs by optimizing the job configuration. Which two actions will help reduce costs? (Choose TWO.)

Accepted Answer

Use G.1X worker type instead of G.2X. Options A and D are correct. Using a smaller DPU count reduces compute cost. Using G.1X worker type (default) is cost-effective; using G.2X would increase cost. Option B (increasing retries) may increase cost. Option C (increasing timeout) has no cost impact. Option E (Spark UI logging) adds cost.

Answer

Increase the job timeout to 48 hours

Answer

Enable Spark UI logging for debugging

Answer

Increase the number of job retries to handle transient failures

Question 8

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that is failing to deliver data to an Amazon S3 bucket. The stream is configured with a Lambda transformation function. The CloudWatch logs show that the Lambda function is timing out. Which action should the engineer take to resolve the issue?

Accepted Answer

Increase the Lambda function timeout setting.. Option C is correct because increasing the Lambda timeout allows the function to complete processing. Option A is wrong because the issue is timeout, not memory. Option B is wrong because the stream configuration does not control Lambda timeout. Option D is wrong because reducing the buffer interval may increase invocation frequency but not fix the timeout.

Answer

Reduce the Firehose buffer interval.

Answer

Decrease the Lambda function's batch size in Firehose.

Answer

Increase the memory allocated to the Lambda function.

Question 9

A data engineer is designing a data ingestion pipeline for clickstream data that arrives in bursts, up to 100 MB/s, and must be processed with exactly-once semantics. The data must be stored in Amazon S3 partitioned by event date and hour. Which combination of services should the engineer use?

Accepted Answer

Amazon Kinesis Data Firehose with S3 destination and dynamic partitioning.. Amazon Kinesis Data Firehose can buffer and batch data to S3 with partitioning. Option A is correct. Option B is wrong because Lambda cold starts can cause latency. Option C is wrong because Glue is for batch ETL, not real-time. Option D is wrong because KCL requires custom application.

Answer

Amazon Kinesis Data Streams with AWS Lambda consumer writing to S3.

Answer

AWS Glue streaming ETL job reading from Amazon MSK and writing to S3.

Answer

Amazon Kinesis Data Streams with KCL application writing to S3.

Question 10

A data engineer has an IAM policy attached to an IAM role used by an AWS Glue job. The Glue job needs to read from S3 bucket 'data-bucket' and write to the same bucket. The job fails with an access denied error when trying to write to S3. What is the issue?

Accepted Answer

The policy does not include s3:ListBucket permission, which Glue may need.. The policy allows s3:PutObject on the bucket, but Glue jobs also need permissions on the bucket itself (s3:ListBucket) for certain operations. Also, the resource is correct. However, the most common issue is that the IAM role does not have permission to decrypt the KMS key if the bucket uses SSE-KMS. But the exhibit does not mention KMS. Option A is wrong because the resource is correct. Option B is wrong because the actions are correct. Option C is correct because the policy does not include s3:ListBucket, which may be required by Glue to list objects. Option D is wrong because Glue can assume roles.

Answer

The Glue job cannot assume the IAM role because of trust policy.

Answer

The resource ARN for S3 is missing the bucket-level permission.

Answer

The actions for S3 are incorrect; s3:PutObject is not sufficient.

Question 11

A data engineer needs to transform JSON data into Parquet format using AWS Glue. The input data has nested fields. Which Glue feature should be used to flatten the nested structure?

Accepted Answer

Relationalize transform. Option B is correct because AWS Glue's 'Relationalize' transform flattens nested JSON into a relational format. Option A is wrong because 'FindMatches' is for deduplication. Option C is wrong because 'DropNullFields' removes null fields. Option D is wrong because 'Map' is for simple field mappings.

Answer

DropNullFields transform

Answer

FindMatches transform

Answer

Map transform

Question 12

A company is ingesting CSV files into Amazon S3. Each file contains a header row. The pipeline uses AWS Glue to crawl the S3 bucket and create a table in the AWS Glue Data Catalog. However, the crawler is including the header as data. What is the most likely cause?

Accepted Answer

The crawler is configured to use a custom classifier that does not skip headers. By default, Glue crawlers do not skip headers unless configured to do so. Adding an explicit 'skip.header.line.count=1' in the table properties fixes it. The crawler classifies correctly but needs the property.

Answer

The S3 bucket has versioning enabled

Answer

The crawler is running in 'crawl all folders' mode

Answer

The CSV files are not compressed

Question 13

A company needs to ingest real-time clickstream data from a web application into Amazon S3 for analytics. The data must be available within minutes of generation. Which AWS service should be used to capture and deliver this streaming data?

Accepted Answer

Amazon Kinesis Data Firehose. Option A (Amazon Kinesis Data Firehose) is correct because it can capture streaming data and deliver it to S3 with minimal latency (typically 60 seconds). Option B (AWS Glue) is a batch ETL service, not real-time. Option C (Amazon SQS) is a message queue, not designed for direct S3 delivery. Option D (Amazon RDS) is a relational database.

Answer

Amazon RDS

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)

Question 14

A data engineer is designing a data ingestion pipeline that uses AWS Lambda to process records from a Kinesis Data Stream and write to DynamoDB. Which TWO strategies can help handle increased throughput and prevent data loss? (Choose TWO.)

Accepted Answer

Configure the Lambda event source mapping with a batch window and set the number of concurrent batches per shard. Options B and D are correct. B: Increasing the number of shards allows higher throughput and Lambda concurrency. D: Configuring the Lambda event source mapping with a batch window and concurrent batches helps manage load. A: Increasing Lambda timeout does not increase throughput. C: Using a DLQ captures failures but does not prevent loss; retries should be configured. E: Using synchronous invocation would block and cause throttling.

Answer

Use synchronous invocation of Lambda from the producer

Answer

Configure a dead-letter queue (DLQ) for the Lambda function

Answer

Increase the Lambda function timeout

Question 15

A data engineer notices that an AWS Glue ETL job is running slower than expected. The job reads from Amazon S3, joins two datasets, and writes the result back to S3. The job uses the default worker type (G.1X) and 10 DPUs. Which action is most likely to improve performance?

Accepted Answer

Repartition the data before the join operation. Option B is correct because increasing the number of partitions (via repartition) can improve parallelism and performance when the data is skewed. Option A (increasing DPUs) may help but is not the most targeted fix. Option C (using coalesce) reduces partitions, which can hurt performance. Option D (changing to G.2X) provides more memory per worker but may not address the core issue of partition skew.

Answer

Increase the number of DPUs to 20

Answer

Use coalesce to reduce the number of output files

Answer

Change the worker type to G.2X