DEA-C01 Practice Test 43 — 15 Questions

Question 1

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 2

A company wants to migrate on-premises data to Amazon S3 using AWS DataSync. The data is stored on an NFS file server and the total volume is 50 TB. The network bandwidth between the on-premises data center and AWS is 1 Gbps (gigabit per second). What is the primary factor that will determine the total time required for the initial data transfer?

Accepted Answer

The available network bandwidth between on-premises and AWS. Option C is correct because the network bandwidth is the bottleneck for transferring 50 TB over a 1 Gbps link; it will take approximately 4.7 days to transfer if bandwidth is fully utilized, but file metadata operations and number of files also matter. Option A is wrong because DataSync can handle large files efficiently. Option B is wrong because the NFS server performance is usually not the bottleneck compared to network. Option D is wrong because S3 object storage can accept data at high throughput.

Answer

The number of S3 buckets used as the destination

Answer

The average file size in the dataset

Answer

The IOPS (I/O operations per second) of the on-premises NFS server

Question 3

A company ingests IoT sensor data into Amazon Kinesis Data Streams. The data must be enriched with device metadata from Amazon DynamoDB and then stored in Amazon S3 in Apache Parquet format. The solution must minimize latency and cost. Which THREE steps should a data engineer implement? (Choose three.)

Accepted Answer

Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.. Option A: Using Kinesis Data Analytics for Flink allows stream enrichment with low latency. Option D: Kinesis Data Firehose can buffer data and convert it to Parquet before delivering to S3. Option E: Kinesis Data Analytics for Flink can look up DynamoDB data for enrichment. Option B (Lambda) has invocation limits and higher cost per record. Option C (Glue) adds latency and cost for streaming. Option F (EMR) is managed and not as seamless.

Answer

Configure an AWS Lambda function to read from the stream, enrich, and write to S3.

Answer

Use AWS Glue streaming ETL to enrich and convert data to Parquet.

Answer

Use Amazon EMR with Spark Streaming to process and store the data.

Question 4

A data engineer runs an AWS Glue crawler that is configured to crawl an S3 bucket named 'my-data-lake' and update the Glue Data Catalog. The crawler fails with an access denied error. The IAM role attached to the crawler has the policy shown in the exhibit. What is the likely cause of the failure?

Accepted Answer

The policy does not allow logging to CloudWatch Logs.. Option B is correct because the crawler needs permission to write logs to CloudWatch Logs; the policy does not include logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents. Option A is wrong because the policy includes s3:ListBucket on the bucket and s3:GetObject on the bucket contents. Option C is wrong because the policy includes glue:CreateTable and glue:UpdateTable. Option D is wrong because the crawler does not need to write to S3.

Answer

The policy does not allow glue:CreateTable on the 'my-data-lake' database.

Answer

The policy does not allow s3:PutObject on the 'my-data-lake' bucket.

Answer

The policy does not allow s3:ListBucket on the 'my-data-lake' bucket.

Question 5

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 6

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

Question 7

A data engineering team is building a pipeline to transform CSV files uploaded to Amazon S3 into Parquet format using AWS Glue. The transformation must be serverless and handle files that arrive at irregular intervals. Which TWO actions should the team take? (Choose two.)

Accepted Answer

Use an AWS Glue ETL job to convert CSV to Parquet.. Option A: AWS Glue is serverless and can transform data. Option C: S3 event notifications can trigger a Lambda function to start the Glue job. Option B (Amazon EMR) is server-based and requires cluster management. Option D (AWS Data Pipeline) is not serverless. Option E (Amazon Redshift Spectrum) queries data in place but does not transform file formats.

Answer

Configure an Amazon EMR cluster with Apache Spark for on-demand transformation.

Answer

Use AWS Data Pipeline to schedule a periodic transformation.

Answer

Use Amazon Redshift Spectrum to convert files during query execution.

Question 8

A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time and then loaded into an Amazon S3 bucket for long-term storage. Which AWS service should be used to perform the transformation and delivery to S3 with minimal operational overhead?

Accepted Answer

Amazon Kinesis Data Firehose. Option B is correct because Kinesis Data Firehose can subscribe to a Kinesis Data Stream, transform data using Lambda or its built-in transformations, and automatically deliver to S3 with no code required for delivery. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not direct delivery to S3. Option C is wrong because AWS Glue is a batch ETL service, not real-time. Option D is wrong because Amazon EMR is a managed Hadoop cluster that requires significant overhead.

Answer

AWS Glue

Answer

Amazon EMR

Answer

Amazon Kinesis Data Analytics

Question 9

A company uses AWS DMS to migrate a 2 TB PostgreSQL database to Amazon Aurora PostgreSQL. The migration is taking longer than expected due to the initial load. Which AWS service can be used to accelerate the initial load by transferring the database files directly?

Accepted Answer

AWS Snowball. Option C is correct because AWS Snowball can be used to transfer large datasets physically, bypassing network constraints. Option A is wrong because S3 Transfer Acceleration speeds up uploads to S3 over the internet, but the data still goes over the network. Option B is wrong because Direct Connect provides a dedicated network connection, but the data still traverses the network. Option D is wrong because Kinesis Data Firehose is for streaming, not bulk data transfer.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Direct Connect

Answer

Amazon Kinesis Data Firehose

Question 10

A data pipeline ingests daily CSV files from an FTP server into an Amazon S3 bucket. The files must be converted to Parquet format and partitioned by date for efficient querying using Amazon Athena. Which AWS service is most suitable for this transformation?

Accepted Answer

AWS Glue. Option D is correct because AWS Glue offers a serverless Spark environment with built-in transformation libraries and can automatically partition data. Option A is wrong because Lambda has a 15-minute timeout and 10 GB memory limit, making it unsuitable for large CSV-to-Parquet conversions. Option B is wrong because EMR requires cluster management. Option C is wrong because Kinesis Data Firehose is for streaming, not batch.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon EMR

Answer

AWS Lambda

Question 11

A data engineering team is ingesting data from multiple sources into Amazon S3 using AWS Glue ETL jobs. The jobs are failing intermittently with the error: 'Task ran out of memory'. The input data size varies widely from 100 MB to 10 GB per job. Which configuration change would best mitigate this issue?

Accepted Answer

Enable job bookmarking to process only incremental data. Option B is correct because enabling job bookmarking in Glue allows the job to process only new data, reducing memory pressure. Option A is wrong because increasing the number of workers adds parallelism but may not address memory per worker. Option C is wrong because switching to Python shell would reduce capability. Option D is wrong because decreasing batch size may help but could slow down processing.

Answer

Increase the number of workers in the Glue job

Answer

Reduce the batch size in the S3 source node

Answer

Change the job type from Spark to Python shell

Question 12

A company wants to ingest real-time clickstream data from a website into Amazon S3 with a maximum latency of 60 seconds. The data volume peaks at 500 MB/s. Which service should they use to buffer and deliver the data to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Option A is correct because Kinesis Data Firehose can buffer incoming data for up to 60 seconds and deliver to S3. Option B is wrong because Kinesis Data Streams stores data for up to 365 days but requires a consumer to write to S3. Option C is wrong because SQS is a message queue, not designed for streaming ingestion. Option D is wrong because Lambda is not a buffer service.

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams

Answer

AWS Lambda

Question 13

A data engineer is designing a pipeline that ingests JSON logs from an application into Amazon S3. The logs contain a timestamp field. The pipeline must partition the data by date in S3 (e.g., year=2024/month=10/day=01). Which approach minimizes transformation effort?

Accepted Answer

Use Amazon Kinesis Data Firehose with dynamic partitioning. Option D is correct because S3 Batch Operations can copy objects and apply prefix changes, but for real-time partitioning, Kinesis Data Firehose with dynamic partitioning is the best approach. The correct answer is actually Kinesis Data Firehose. However, since the options are limited, the correct answer is D (Kinesis Data Firehose) as it can automatically partition data based on the timestamp. Option A is wrong because Lambda would require custom code. Option B is wrong because Athena is a query service. Option C is wrong because Glue crawlers create metadata, not partitions.

Answer

Use AWS Glue crawlers to infer schema and create partitions

Answer

Use AWS Lambda to process each object and copy to the appropriate prefix

Answer

Use Amazon Athena to create partitions on the existing data

Question 14

A streaming application sends data to Amazon Kinesis Data Streams. The data must be enriched with reference data from an Amazon DynamoDB table in real-time. Which AWS service can be used to perform this enrichment with minimal latency?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Option B is correct because Kinesis Data Analytics for Apache Flink can process streaming data and join with DynamoDB in real-time. Option A is wrong because Lambda can enrich but may add latency due to cold starts. Option C is wrong because Kinesis Data Firehose does not support real-time enrichment from DynamoDB. Option D is wrong because Glue is batch-oriented.

Answer

Amazon Kinesis Data Firehose with Lambda transformation

Answer

AWS Lambda function triggered by Kinesis Data Streams

Answer

AWS Glue streaming ETL

Question 15

A data engineering team is designing a data ingestion pipeline for a social media analytics platform. The pipeline must handle up to 100,000 events per second with less than 1 second processing latency. Which TWO services should be used together to meet these requirements?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. A (Kinesis Data Streams) provides the high-throughput ingestion layer, and C (Kinesis Data Analytics for Apache Flink) provides low-latency stream processing. B (SQS) is a queue, not optimized for high-throughput streams. D (Firehose) has higher latency (buffering). E (Glue) is batch-oriented.

Answer

AWS Glue streaming ETL

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS