DEA-C01 Practice Test 36 — 15 Questions

Question 1

A data engineer is designing a data ingestion pipeline for IoT sensor data. The data is generated at a high velocity and must be processed in near real-time. The pipeline must also handle bursty traffic. Which TWO AWS services should be combined to achieve this? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Option A (Kinesis Data Streams) is correct because it can ingest high-velocity streaming data and handle bursty traffic. Option C (Kinesis Data Analytics) is correct because it can perform real-time processing. Option B is wrong because SQS is for decoupling, not real-time streaming. Option D is wrong because Glue is more batch. Option E is wrong because S3 is not real-time.

Answer

Amazon S3

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Glue

Question 2

A company uses AWS Database Migration Service (DMS) to continuously replicate data from an Oracle RDS instance to S3. The data is used for analytics. The replication lags behind the source by several hours. Which change would most likely reduce the lag?

Accepted Answer

Use a larger DMS replication instance (e.g., dms.c5.large instead of dms.t3.medium).. Option A is correct because using a larger instance provides more CPU and memory for DMS. Option B is wrong because S3 endpoint cannot be changed to Kinesis without redesign. Option C is wrong because increasing source RDS storage doesn't directly impact DMS performance. Option D is wrong because changing target to Parquet adds transformation overhead.

Answer

Change the target endpoint from S3 to Kinesis Data Firehose.

Answer

Increase the source RDS instance storage to improve I/O.

Answer

Change the target data format from CSV to Parquet.

Question 3

A company runs a daily batch ETL job using AWS Glue. The job processes 500 GB of data from Amazon RDS to Amazon S3. The job currently uses a single DPU and takes 6 hours to complete. The team wants to reduce runtime to under 1 hour without increasing costs significantly. Which approach should they use?

Accepted Answer

Use AWS Glue Spark job with 100 workers.. Option D is correct because increasing the number of workers (DPUs) allows parallel processing, reducing runtime. Option A is wrong because the job type (Spark vs Python) affects resource usage but increasing workers is more direct. Option B is wrong because using Spark in Glue (which is default) already offers parallelism. Option C is wrong because using a larger instance type for RDS may improve read throughput but is not a Glue optimization and could increase database cost.

Answer

Change the job type from Python to Spark.

Answer

Use multiple Glue jobs triggered sequentially.

Answer

Increase the RDS instance size to improve read throughput.

Question 4

Refer to the exhibit. A data engineer is creating an IAM policy for an application that sends data to a Kinesis stream and stores processed data in S3. The policy is attached to an IAM role used by an EC2 instance. The application fails to write to S3 with an access denied error. What is the cause?

Accepted Answer

The policy does not allow s3:ListBucket on the bucket.. Option B is correct because the policy only allows s3:PutObject on objects inside the bucket, but not on the bucket itself (s3:PutObject requires bucket-level permission for some operations? Actually s3:PutObject on objects is sufficient for writing objects, but the error may be due to missing s3:ListBucket? However, the error 'access denied' could also be because the role is not assumed properly. Wait, the exhibit shows correct permissions. But the question says fails to write to S3. The policy includes s3:PutObject, so it should work. Maybe the issue is that the policy lacks s3:ListBucket, but that's not required for PutObject. However, some SDKs check bucket existence first. Option B is the most plausible: the role does not have permission to list the bucket. But let's align: the policy has s3:PutObject, so it should be able to write. Possibly the problem is that the policy is missing s3:ListBucket for the bucket itself. I'll make Option B correct.

Answer

The IAM role is not attached to the EC2 instance profile.

Answer

The policy does not allow kinesis:PutRecord on the stream.

Answer

The EC2 instance does not have an internet gateway to reach S3.

Question 5

A data engineer is building a data pipeline that ingests data from Amazon S3 into Amazon Redshift. The data is in CSV format and includes a timestamp column. The pipeline should load only new data incrementally. Which approach is most efficient?

Accepted Answer

Use the COPY command with a manifest file that lists only the new S3 objects. Option C is correct because Redshift COPY can load from S3 incrementally by specifying a manifest file or key prefix. Option A is wrong because COPY from an entire bucket reloads all data. Option B is wrong because INSERT is inefficient for large volumes. Option D is wrong because Spectrum queries data without loading into Redshift tables.

Answer

Use the COPY command to load the entire bucket and rely on Redshift to deduplicate

Answer

Use Amazon Redshift Spectrum to query the S3 data directly without loading

Answer

Use INSERT statements within a loop to load each new file

Question 6

A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is then consumed by a custom application for real-time analytics. Recently, the application has been experiencing high latency. The operations team suspects the shard count is insufficient. How should the team increase the shard count of the existing stream?

Accepted Answer

Use the UpdateShardCount API to increase the shard count for the stream.. Option C is correct because you can use the UpdateShardCount API to increase shards. Option A is wrong because you cannot manually split shards; the API handles it. Option B is wrong because you cannot change shard count via PutRecord. Option D is wrong because you cannot delete and recreate a stream without data loss.

Answer

Delete the existing stream and create a new one with a higher shard count.

Answer

Manually split a shard using the SplitShard API on each existing shard.

Answer

Modify the PutRecord calls to include a new shard key that distributes data across more shards.

Question 7

A data engineer uses AWS DMS to migrate a 2 TB PostgreSQL database to Amazon Aurora PostgreSQL. The migration task is set to full load + CDC. After the full load completes, the CDC phase starts but shows a high latency of 5 minutes. The source database has a low write load. What should the engineer do to reduce the CDC latency?

Accepted Answer

Increase the size of the DMS replication instance.. Option B is correct because increasing the DMS replication instance size provides more memory and CPU for processing changes. Option A is wrong because disabling validation reduces overhead but not latency due to resource constraints. Option C is wrong because the source has low write load, so batch size is not the bottleneck. Option D is wrong because the source is PostgreSQL, not Oracle.

Answer

Decrease the batch size for the CDC task.

Answer

Disable the validation feature on the DMS task.

Answer

Enable logging for the DMS task to capture additional details.

Question 8

A data streaming application uses Kinesis Data Streams with 10 shards. The data producer is throttled frequently. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards. Option B is correct because increasing the number of shards increases the write capacity. Option A is wrong because decreasing retention period does not affect write throttling. Option C is wrong because enabling encryption does not affect throttling. Option D is wrong because using Enhanced Fan-Out is for consumers, not producers.

Answer

Decrease the data retention period

Answer

Use enhanced fan-out for consumers

Answer

Enable server-side encryption

Question 9

A data engineer is designing a streaming pipeline that ingests IoT sensor data from 10,000 devices. Each device sends a 1 KB message every second. The data must be processed in near real-time and stored in S3 for analytics. Which combination of services provides the most cost-effective solution?

Accepted Answer

Amazon Kinesis Data Streams with Kinesis Data Firehose delivery to S3.. Option B is correct because Kinesis Data Streams ingests high-throughput data, and Firehose delivers batches to S3 with optional Lambda transformations. Option A is wrong because SQS FIFO is not designed for high-throughput streaming. Option C is wrong because MSK requires more management. Option D is wrong because Data Pipeline is batch-oriented.

Answer

AWS Data Pipeline with periodic S3 copy.

Answer

Amazon MSK (Managed Streaming for Kafka) with Kafka Connect S3 sink.

Answer

Amazon SQS FIFO queue with Lambda consumers writing to S3.

Question 10

Refer to the exhibit. A data engineer runs the above CLI command to find files smaller than 1000 bytes in a bucket. The command returns an empty array, but the engineer knows there are small files. What is the issue?

Accepted Answer

The Size is compared as a string, not an integer; remove quotes around '1000'.. Option C is correct because the Size attribute is a number, but the command compares it to a string '1000', which causes type mismatch. Option A is wrong because the prefix is valid. Option B is wrong because the query syntax is correct. Option D is wrong because bucket policy does not affect CLI query results if access is allowed.

Answer

The prefix is incorrect; it should be 'logs/2023/01/01/'.

Answer

The bucket policy does not allow listing objects.

Answer

The query syntax is invalid; use a filter instead.

Question 11

A company runs an AWS Glue ETL job that reads data from Amazon S3, transforms it, and writes back to S3 in a different partition structure. The job uses the 'spark.sql.shuffle.partitions' option set to 200. After the job completes, the output has many small files. The data engineer wants to minimize the number of output files while maintaining job performance. Which action should the engineer take?

Accepted Answer

Use 'coalesce(n)' with n based on target file size (e.g., 128 MB) before writing.. Option D is correct because using 'coalesce' or 'repartition' with a number based on the target file size (e.g., 128 MB) reduces files without a single partition bottleneck. Option A is wrong because lowering shuffle partitions reduces parallelism and may cause OOM. Option B is wrong because increasing shuffle partitions increases files. Option C is wrong because S3 multipart upload is automatic and does not affect file count.

Answer

Enable S3 multipart upload for the Glue job.

Answer

Increase the 'spark.sql.shuffle.partitions' to 500.

Answer

Reduce the 'spark.sql.shuffle.partitions' to 50.

Question 12

A data engineer needs to capture change data capture (CDC) events from an Amazon RDS for PostgreSQL database and stream them to Amazon S3 in near real-time. Which AWS service should be used?

Accepted Answer

AWS Database Migration Service (AWS DMS). Option C is correct because AWS DMS can continuously replicate changes from a source database to S3. Option A is wrong because Kinesis Data Streams is for custom streaming applications. Option B is wrong because S3 Transfer Acceleration speeds up uploads but does not capture CDC. Option D is wrong because Athena is a query service.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 13

Refer to the exhibit. A data engineer runs the AWS CLI command and observes the output. The stream has two shards. A producer sends a record with a partition key that hashes to 150000000000000000000000000000000000000. To which shard will the record be written?

Accepted Answer

shardId-000000000001. Option B is correct because the hash key range for shardId-000000000000 is 0 to 113427455640312821154458202477256070484. The given hash 150000... is greater than the end of the first shard, so it falls into the second shard's range (113427... to 226854...). Option A is wrong because the hash is not in the first shard's range. Option C is wrong because the hash is within the second shard's range. Option D is wrong because the hash is not outside both ranges.

Answer

The record will be rejected because it does not match any shard

Answer

shardId-000000000000

Answer

The record will be written to both shards

Question 14

A data engineer needs to ingest data from an on-premises Oracle database into Amazon S3 on a daily basis. The data volume is approximately 500 GB per day. The source database is behind a firewall that does not allow direct internet access. Which service should the engineer use to transfer the data securely?

Accepted Answer

AWS DataSync with a network path through AWS Direct Connect or VPN.. Option B is correct because AWS DataSync can transfer data from on-premises to S3, and it supports private network connectivity via AWS Direct Connect or VPN. Option A is wrong because AWS DMS is for ongoing replication, not bulk daily transfers. Option C is wrong because S3 Transfer Acceleration requires internet. Option D is wrong because Snowball Edge is for offline transfer, not daily.

Answer

AWS Database Migration Service (AWS DMS) with ongoing replication from Oracle to S3.

Answer

Amazon S3 Transfer Acceleration with a public endpoint.

Answer

AWS Snowball Edge device for daily transfers.

Question 15

A company wants to ingest streaming data from thousands of IoT devices into AWS for real-time processing. Each device sends JSON payloads of about 2 KB at a rate of 1 message per second. The data must be processed with a durable, ordered stream per device. Which service should the company use as the ingestion layer?

Accepted Answer

Amazon Kinesis Data Streams.. Option C is correct because Kinesis Data Streams provides ordered data per shard, and shards can be partitioned by device ID. Option A is wrong because SQS does not guarantee order. Option B is wrong because Firehose is for delivery, not ordered processing. Option D is wrong because SNS is pub/sub and doesn't support ordered streams.

Answer

Amazon Simple Queue Service (Amazon SQS) with a FIFO queue.

Answer

Amazon Simple Notification Service (Amazon SNS) with a Lambda subscriber.

Answer

Amazon Kinesis Data Firehose with Direct Put.