DEA-C01 Practice Test 35 — 15 Questions

Question 1

A company needs to ingest real-time clickstream data from a web application into Amazon Redshift with minimal latency. The data volume is high and requires processing before loading. Which architecture is MOST appropriate?

Accepted Answer

Kinesis Data Streams -> Kinesis Data Firehose -> Redshift. Option A is correct because Kinesis Data Firehose can deliver streaming data to Redshift with built-in transformation via Lambda. Option B is wrong because S3 does not provide real-time ingestion. Option C is wrong because Glue is batch-oriented. Option D is wrong because DynamoDB Streams is for DynamoDB changes, not clickstream.

Answer

AWS Glue ETL jobs scheduled every 5 minutes -> Redshift

Answer

S3 -> Lambda -> Redshift

Answer

DynamoDB Streams -> Lambda -> Redshift

Question 2

A data pipeline ingests streaming data from Kinesis Data Streams into S3 via Kinesis Data Firehose. Occasionally, small files are written to S3, increasing downstream processing costs. What is the most efficient way to reduce the number of small files?

Accepted Answer

Increase the Firehose buffering interval to 300 seconds and buffering size to 64 MB.. Option C is correct because increasing the buffering interval and size in Firehose batches more data into fewer files. Option A is wrong because Lambda can aggregate but adds complexity and cost. Option B is wrong because KCL runs on EC2, not serverless. Option D is wrong because Glue ETL runs after delivery, not preventing small files.

Answer

Use a Lambda function to aggregate records before sending to Firehose.

Answer

Use the Kinesis Client Library (KCL) to write larger batches to S3 directly.

Answer

Run a daily AWS Glue job to concatenate small files.

Question 3

A data engineering team needs to ingest streaming data from thousands of IoT devices and store it in Amazon S3 for batch processing. The data arrives at a rate of 10 MB/s, with occasional spikes up to 50 MB/s. The data must be processed in near real-time with minimal latency. Which AWS service should be used for ingestion?

Accepted Answer

Amazon Kinesis Data Streams. Option C is correct because Kinesis Data Streams can handle high throughput and is designed for real-time ingestion. Option A is wrong because SQS is for decoupled messaging, not high-throughput streaming; it also lacks built-in S3 integration. Option B is wrong because S3 is a storage service, not an ingestion endpoint. Option D is wrong because DynamoDB Streams captures changes to DynamoDB tables, not external IoT data.

Answer

Amazon DynamoDB Streams

Answer

Amazon SQS

Answer

Amazon S3

Question 4

A data engineer is designing a data pipeline that ingests streaming data from Kinesis Data Streams, transforms it using AWS Lambda, and writes to S3. The Lambda function sometimes fails due to transient errors, and the engineer wants to ensure no data is lost. Which approach should be used?

Accepted Answer

Use Kinesis Data Firehose as the delivery stream with Lambda for transformation and configure error handling with retries and a backup S3 bucket. Option D (Use Kinesis Data Firehose as the delivery stream with Lambda for transformation and configure error handling with retries and a backup S3 bucket) is correct because Firehose provides built-in retry and error handling. Option A (Increase Lambda timeout) does not solve transient errors. Option B (Use a DLQ) helps capture failures but does not automatically retry. Option C (Use Kinesis Client Library) requires more custom code.

Answer

Use the Kinesis Client Library to process records with checkpointing

Answer

Increase the Lambda function's timeout and memory

Answer

Configure a dead-letter queue (DLQ) on Lambda to capture failed records

Question 5

A company uses AWS Glue ETL jobs to transform data from Amazon RDS to Amazon S3 daily. The job recently started failing with memory errors. The data volume has grown 3x in the past month. Which change should the data engineer make to resolve the issue?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Option D is correct because increasing the number of DPUs (Data Processing Units) in the AWS Glue job provides more memory and compute resources, which can handle larger data volumes. Option A (increasing RDS instance size) may not help if the bottleneck is the Glue job. Option B (switching to Spark) is not directly relevant; Glue already uses Spark. Option C (partitioning S3 output) improves query performance but does not fix memory errors during transformation.

Answer

Increase the size of the Amazon RDS instance

Answer

Switch the Glue job type from Python Shell to Spark

Answer

Partition the output data in Amazon S3 by date

Question 6

A data engineer is designing a data transformation pipeline using AWS Glue. The source data is in Amazon S3 in Parquet format, and the transformed output must be written to another S3 bucket in Parquet format partitioned by year, month, day. The pipeline should handle incremental updates efficiently. Which three features should the engineer use? (Choose THREE.)

Accepted Answer

AWS Glue job bookmarks to track processed data. Options A, B, and D are correct. Bookmarks track processed data for incremental jobs. DynamicFrames are flexible for schema evolution. Partition pruning helps read only relevant partitions. Option C (Spark SQL) is not specific to incremental. Option E (JobWatch) is not an AWS Glue feature.

Answer

Use AWS Glue JobWatch for monitoring job progress

Answer

Use Spark SQL for transformations

Question 7

A company uses AWS Lake Formation to manage data lake permissions. A data engineer needs to grant an IAM role 'Read' access to a specific database and all its tables in the Data Catalog. What is the MOST efficient way to achieve this?

Accepted Answer

Grant 'Select' on the database and select 'Include' to apply to all tables. Lake Formation allows granting permissions on the database with 'Include' option to apply to all tables. This is the most efficient method for granting access to all tables.

Answer

Grant 'Super' permission on the Data Catalog

Answer

Add the IAM role to the Lake Formation administrators group

Answer

Grant 'Describe' on the database and 'Select' on each table individually

Question 8

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The Flink application reads from a Kinesis Data Streams source, performs aggregations, and writes results to Amazon S3. The application is experiencing high checkpoint failures, and the processing lag is increasing. The data volume is 50 MB/s with an average record size of 1 KB. Which TWO actions would improve checkpoint reliability and reduce lag? (Choose TWO.)

Accepted Answer

Increase the checkpoint interval in the Flink configuration.. Options A and D are correct. Increase the checkpoint interval to reduce frequency, and increase parallelism with more KPUs. Option B is wrong because reducing parallelism would worsen lag. Option C is wrong because decreasing checkpoint interval increases failures. Option E is wrong because Kinesis Data Firehose is not a direct solution to checkpoint failures.

Answer

Decrease the checkpoint interval to complete checkpoints faster.

Answer

Replace the S3 sink with Kinesis Data Firehose.

Answer

Decrease the parallelism of the Flink application.

Question 9

A company wants to ingest streaming data from thousands of IoT devices into AWS for real-time analytics. Which AWS service is best suited for this purpose?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time streaming data ingestion from many sources. Option A is wrong because S3 is for object storage, not real-time streaming. Option B is wrong because Lambda processes data but is not a primary ingestion service. Option D is wrong because RDS is a relational database, not for streaming ingestion.

Answer

Amazon S3

Answer

AWS Lambda

Answer

Amazon RDS

Question 10

A company uses AWS Glue to transform data stored in S3. The Glue job runs daily and processes data in the range of hundreds of GB. The data engineer wants to optimize the job for cost and performance. Which THREE actions should be taken? (Choose THREE.)

Accepted Answer

Increase the number of DPUs for the job. Option A is correct because using columnar formats like Parquet improves performance and reduces data scanned. Option B is correct because partitioning reduces data processed. Option D is correct because increasing the number of DPUs can speed up the job, but must be balanced with cost. Option C is wrong because S3 is already the source; moving to HDFS is not relevant. Option E is wrong because reducing DPUs would increase runtime.

Answer

Store intermediate data in HDFS on Amazon EMR

Answer

Reduce the number of DPUs to save cost

Question 11

A data engineer needs to ingest data from an external FTP server into S3 on a schedule. The FTP server is only accessible via VPN. Which AWS service is best suited for this task?

Accepted Answer

AWS Transfer Family. Option D is correct because AWS Transfer Family supports FTP over VPN. Option A is wrong because DataSync requires network connectivity but not FTP protocol. Option B is wrong because Glue can read from FTP but requires custom connectors. Option C is wrong because Snowcone is for offline data transfer.

Answer

AWS Snowcone

Answer

AWS Glue with a Python shell

Answer

AWS DataSync

Question 12

A company needs to transfer 10 TB of historical data from an on-premises HDFS cluster to Amazon S3. The data is stored on a single 20 TB disk. The network link to AWS has a bandwidth of 1 Gbps. The transfer must be completed within 2 days. Which solution meets these requirements?

Accepted Answer

Use AWS Snowball Edge to transfer the data physically.. Option B is correct because AWS Snowball Edge can physically ship the data, bypassing network bandwidth limits. Option A is wrong because 1 Gbps for 2 days can only transfer about 21.6 TB, which is enough, but network stability and other traffic may cause delays. Option C is wrong because AWS DMS is for database migration, not HDFS. Option D is wrong because Kinesis is for streaming, not bulk transfer.

Answer

Use Amazon Kinesis Data Streams to stream data to S3.

Answer

Use AWS DMS to migrate data from HDFS to S3.

Answer

Use AWS CLI to copy data directly to S3 over the network.

Question 13

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis stream with 10 shards and writes to an S3 bucket. The application is experiencing high latency. Analysis shows that the application is not keeping up with the incoming data rate. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the Parallelism of the Flink application. Increasing the parallelism of the Flink application allows it to process more data concurrently. This directly addresses the processing bottleneck.

Answer

Increase the number of shards in the Kinesis stream

Answer

Enable exactly-once delivery to S3

Answer

Use a larger Kinesis Data Analytics application (increase KPU)

Question 14

A data engineer is building a batch ETL pipeline using AWS Glue. The source data is in Amazon RDS for MySQL. The pipeline must run daily and process only new and modified records since the last run. The engineer needs to implement change data capture (CDC) efficiently. Which THREE steps should the engineer take? (Choose THREE.)

Accepted Answer

Configure the Glue job to use a JDBC connection with a SQL query that reads from the binary log.. Options A, C, and D are correct. Enable binary logging for CDC, use a bookmark to track processed records, and create a job bookmark in Glue. Option B is wrong because querying entire table daily is inefficient. Option E is wrong because Kinesis Data Streams is for real-time, not batch CDC.

Answer

Ingest the RDS data into Kinesis Data Streams and then use Glue.

Answer

Use a full table scan with a WHERE clause on a timestamp column.

Question 15

A company uses AWS Glue to run ETL jobs that transform data from Amazon S3 (Parquet) into a denormalized format for Amazon Redshift. The Glue job uses the DynamicFrame API. The job is failing with a 'MemoryError' when performing a join operation. The data is skewed on the join key. Which THREE actions can reduce memory usage and improve job stability? (Choose THREE.)

Accepted Answer

Use a broadcast join if one of the tables is small enough.. Options B, C, and D are correct. Salting the join key distributes skew, using broadcast join for a smaller table reduces shuffle, and splitting the job into multiple steps reduces memory per stage. Option A is wrong because increasing DPUs may help but is not a targeted solution. Option E is wrong because repartitioning does not fix skew and may increase overhead.

Answer

Increase the number of DPUs for the Glue job.

Answer

Repartition the data on the join key before the join operation.