DEA-C01 Timed Practice Test 1 — 10 Questions

Question 1

A company uses Amazon Kinesis Data Firehose to ingest application logs into an Amazon S3 bucket. The logs are in JSON format. The data engineering team wants to convert the logs from JSON to Parquet format before landing in S3. What is the most cost-effective way to achieve this?

10 minute time limit — choose an answer to begin.

Accepted Answer

Configure the Firehose delivery stream to convert the data to Parquet using a schema from AWS Glue.. Kinesis Data Firehose can convert the input data format to Parquet using a schema from AWS Glue. Option A is incorrect because Athena queries raw data and would not help with ingestion. Option C is incorrect because Lambda can do this but would incur additional compute cost. Option D is incorrect because Glue ETL would add cost and latency.

Answer

Use Amazon Athena to query the JSON data and write results in Parquet format.

Answer

Use an AWS Lambda function to transform each record to Parquet and send to Firehose.

Answer

Use an AWS Glue ETL job to run on a schedule and convert JSON to Parquet in S3.

Question 2

A data engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that the application's checkpointing is failing intermittently, causing data reprocessing. The application uses a large state. Which configuration change should the team make to improve checkpoint reliability?

Accepted Answer

Increase the checkpointing interval.. Increasing the checkpointing interval reduces the frequency of checkpoint operations, giving the system more time to complete each checkpoint before the next one starts. This alleviates backpressure and resource contention, which is critical when dealing with large state, as checkpointing large state is I/O and CPU intensive and can fail if intervals are too tight.

Answer

Disable checkpointing to avoid failures.

Answer

Switch the state backend from in-memory to RocksDB.

Answer

Increase the parallelism of the application.

Question 3

A data engineer is troubleshooting a Kinesis Data Analytics application that processes streaming data. The application is falling behind and has a high 'MillisBehindLatest' metric. The application uses a parallelism of 2. The source stream has 4 shards. What is the MOST likely cause and solution?

Accepted Answer

The application parallelism is too low; increase it to 4.. The 'MillisBehindLatest' metric indicates the application is not keeping up with the incoming data. With a source stream of 4 shards and a parallelism of only 2, the application cannot process data from all shards concurrently, leading to backpressure. Increasing parallelism to match the shard count (4) allows each shard to be processed by a separate task, reducing lag.

Answer

The application is using a JSON format; switch to Avro.

Answer

The source stream has too many shards; decrease to 2.

Answer

The output destination is slow; change to a faster sink.

Question 4

A company is using Amazon DynamoDB with on-demand capacity for a gaming application that experiences unpredictable traffic spikes. The application consistently sees 'ProvisionedThroughputExceededException' errors during spikes. The data engineer needs to resolve this issue without changing the application code. What should the engineer do?

Accepted Answer

Switch the table to on-demand capacity mode. The application is already using on-demand capacity, but the error 'ProvisionedThroughputExceededException' indicates the table is actually in provisioned mode, not on-demand. Switching to on-demand capacity mode eliminates throttling by automatically scaling throughput to match traffic spikes, with no code changes required.

Answer

Enable DynamoDB Accelerator (DAX) to cache read requests

Answer

Increase the read capacity units

Answer

Enable auto scaling for the table with a higher maximum capacity

Question 5

A company is using Amazon S3 to store sensitive data. They need to automatically transition objects to S3 Glacier Deep Archive after 90 days and delete them after 7 years. Which S3 lifecycle configuration action should be used?

Accepted Answer

Transition. The S3 lifecycle 'Transition' action is specifically designed to move objects between storage classes. To automatically move objects to S3 Glacier Deep Archive after 90 days, you define a transition rule with a 'Days' value of 90 and a 'StorageClass' of 'DEEP_ARCHIVE'. This action directly meets the requirement for transitioning data to a colder storage tier.

Answer

AbortIncompleteMultipartUpload

Answer

Expiration

Answer

NoncurrentVersionExpiration

Question 6

A company is using Amazon RDS for SQL Server with Multi-AZ. The database has a 500 GB data file and 100 GB log file. The application experiences high latency during peak hours. Monitoring shows high WriteIOPS on the primary. Which change will reduce latency without losing the ability to failover?

Accepted Answer

Increase the provisioned IOPS on the RDS instance. The high WriteIOPS on the primary indicates that the storage layer is saturated, causing latency. Increasing provisioned IOPS on the RDS instance directly addresses the bottleneck by providing more I/O capacity, and since Multi-AZ is already enabled, the standby remains synchronized and failover capability is preserved.

Answer

Reduce the log file size by changing recovery model

Answer

Create a Read Replica in a different Availability Zone

Answer

Switch to Multi-AZ with two readable standbys

Question 7

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The database experiences intermittent slowdowns during peak hours. The company's DevOps team suspects that the primary instance is overwhelmed. Which action should the team take to distribute the read load without changing the application code?

Accepted Answer

Create a read replica and modify the connection string to point to the replica for read queries.. Creating a read replica and modifying the connection string to point to the replica for read queries (Option B) offloads read traffic from the primary RDS instance without requiring application code changes. This directly addresses the intermittent slowdowns during peak hours by distributing the read load, leveraging MySQL’s native replication to keep the replica synchronized. The key constraint is 'without changing the application code,' which is satisfied by simply updating the connection string in the application configuration.

Answer

Increase the instance size of the RDS instance.

Answer

Enable Multi-AZ on the existing instance.

Answer

Configure DynamoDB Accelerator (DAX) in front of the RDS instance.

Question 8

Refer to the exhibit. A data engineer notices that the Redshift cluster 'mycluster' does not have automated backups beyond 7 days. However, the compliance team requires a minimum of 35 days of backup retention. What should the engineer do?

Accepted Answer

Modify the cluster's automated snapshot retention period to 35 days.. Amazon Redshift allows you to modify the automated snapshot retention period for a cluster up to 35 days. The engineer can use the AWS Management Console, CLI, or API to change the `automated_snapshot_retention_period` parameter from the current 7 days to 35 days, meeting the compliance requirement without additional manual intervention.

Answer

Change the node type to ra3.xlplus to enable automatic backups for 35 days.

Answer

Enable audit logging to capture changes for recovery.

Answer

Take manual snapshots every day and retain them for 35 days.

Question 9

A company is evaluating Amazon DynamoDB for a new application. The application requires single-digit millisecond latency for read and write operations. Which TWO DynamoDB features should the company enable to achieve this? (Choose TWO.)

Accepted Answer

Use DAX with Write-Through caching.. DAX with Write-Through caching ensures that every write to DynamoDB is also written to the DAX cache, so subsequent reads of the same item are served from the in-memory cache with single-digit millisecond latency. Option D is correct because DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache that reduces read response times from single-digit milliseconds to microseconds, directly meeting the latency requirement for read operations.

Answer

Enable DynamoDB Global Tables.

Answer

Enable DynamoDB Streams.

Answer

Enable auto-scaling for read and write capacity.

Question 10

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. Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it supports stateful stream processing with exactly-once semantics, can maintain state across multiple records, and has no 15-minute execution limit like AWS Lambda. It natively integrates with Kinesis Data Streams and can sink transformed data to S3, meeting all requirements for complex stateful operations.

Answer

Amazon EMR running Spark Structured Streaming

Answer

Amazon Kinesis Data Firehose with Lambda transformation

Answer

AWS Glue streaming ETL job