DEA-C01 Practice Exam 61 — 20 Questions

Question 1

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.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is correct because it provides a durable, scalable, real-time data ingestion layer that can handle variable throughput by sharding, and it retains data for up to 365 days, enabling reprocessing of failed records by replaying from a specific sequence number or timestamp.

Answer

Amazon SQS

Answer

Amazon DynamoDB Streams

Answer

Amazon RDS

Question 2

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is transformed using an AWS Lambda function. Some records fail transformation and are lost because the Lambda function throws an exception. The data engineer needs to capture the failed records for analysis without affecting the pipeline. What should the engineer do?

Accepted Answer

Configure the Firehose delivery stream to send failed records to a backup S3 bucket. Amazon Kinesis Data Firehose natively supports a 'backup' or 'error output' configuration: when a Lambda transformation fails (e.g., throws an exception), Firehose can automatically route the failed records to a designated Amazon S3 bucket for failed data. This allows the engineer to capture and analyze the failed records without blocking or altering the main delivery pipeline, as the stream continues processing successfully transformed records.

Answer

Increase the buffer size of the Firehose stream

Answer

Disable the Lambda transformation and process all records in batch later

Answer

Modify the Lambda function to write failed records to Amazon DynamoDB

Question 3

A data engineer sees the CloudWatch log entry in the exhibit for a Lambda function that processes data from an Amazon SQS queue. What is the MOST likely cause of the timeout?

Accepted Answer

The Lambda function's timeout is too short for the processing required.. The CloudWatch log shows the Lambda function timed out after 30 seconds (duration 30001.23 ms), which is the default timeout. Increasing the timeout allows the function to complete its processing. Option A is incorrect because reserved concurrency affects throughput, not a single function's timeout. Option B is incorrect because memory usage is low (64 MB out of 128 MB), so memory is not the issue. Option D is incorrect because the SQS visibility timeout controls how long a message is hidden after being picked up, but the Lambda timeout is independent.

Answer

The Lambda function's reserved concurrency is set too low.

Answer

The Lambda function is running out of memory.

Answer

The SQS queue's visibility timeout is set too low.

Question 4

A company uses Amazon EMR to run Spark jobs on a transient cluster. The jobs are submitted via a step in the cluster. The cluster is configured to auto-terminate after the last step completes. However, the cluster is not terminating even though the step shows as 'COMPLETED'. What could be the cause?

Accepted Answer

The step's 'ActionOnFailure' parameter is set to 'CONTINUE' and 'KeepClusterAliveOnFailure' is true.. In Amazon EMR, the step-level parameter `KeepClusterAliveOnFailure` (or `KeepJobFlowAliveWhenNoSteps` for the cluster) can prevent auto-termination. When `ActionOnFailure` is set to `CONTINUE` and `KeepClusterAliveOnFailure` is `true`, the cluster remains running even after the last step completes, overriding the transient cluster's auto-terminate setting. Option A is incorrect because root device size does not affect termination. Option B is incorrect because the step status 'COMPLETED' indicates success, not a bug. Option C is incorrect because the cluster is explicitly configured as transient and auto-terminating, not long-running.

Answer

The cluster's root device size is too large.

Answer

The step failed with an error, but the status shows 'COMPLETED' due to a reporting bug.

Answer

The cluster is configured as a long-running cluster.

Question 5

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed into Parquet format and stored in Amazon S3. Which AWS service can perform the transformation in near real-time with minimal operational overhead?

Accepted Answer

Amazon Kinesis Data Firehose with a transformation Lambda function. Amazon Kinesis Data Firehose is the fully managed service designed to load streaming data into S3 with built-in data format conversion. By attaching a Lambda transformation function, you can convert incoming records to Parquet format in near real-time without managing any infrastructure, making it the lowest-operational-overhead choice for this task.

Answer

Amazon EMR cluster running Spark Streaming

Answer

AWS Glue ETL job triggered by Kinesis stream

Answer

Amazon Kinesis Data Analytics for Apache Flink

Question 6

A data engineer is migrating an on-premises Microsoft SQL Server database to Amazon RDS for SQL Server. The database is 2 TB in size and has a 4-hour maintenance window. The company needs to minimize downtime and ensure data consistency. Which TWO methods should the engineer use? (Choose TWO.)

Accepted Answer

Use AWS Database Migration Service (AWS DMS) with ongoing replication to minimize downtime.. AWS DMS with ongoing replication (change data capture) is correct because it allows continuous synchronization from the on-premises SQL Server to Amazon RDS for SQL Server, minimizing downtime by keeping the target database up-to-date until the final cutover. This approach ensures data consistency by capturing and applying ongoing changes without requiring a long outage window.

Answer

Use SQL Server Management Studio (SSMS) export wizard to transfer data.

Answer

Use AWS Schema Conversion Tool (AWS SCT) to convert the schema and migrate data.

Answer

Export the database to CSV files and use BULK INSERT to load into RDS.

Question 7

A company uses AWS Glue to process data from Amazon RDS MySQL into Amazon S3. The Glue job uses a JDBC connection and runs on a schedule. Recently, the job has been failing with a 'Communications link failure' error. The RDS instance is in a private subnet. Which troubleshooting step should the data engineer take FIRST?

Accepted Answer

Verify that the Glue job's VPC subnet and security group allow outbound traffic to RDS.. The 'Communications link failure' error typically indicates a network connectivity issue between the Glue job and the RDS instance. Since the RDS instance is in a private subnet, the Glue job must be configured with a VPC subnet and security group that allows outbound traffic to the RDS instance's security group on port 3306 (MySQL). Without this network path, the JDBC connection cannot be established, making verifying the VPC and security group configuration the first logical troubleshooting step.

Answer

Check the Glue job's DPU allocation; increase if too low.

Answer

Review the Glue job script for data type mismatches.

Answer

Increase the RDS instance's max_connections parameter.

Question 8

A company uses AWS Glue to transform data in S3. The transformation job reads Parquet files, filters rows, and writes to another S3 bucket. The job takes longer than expected. Which change would MOST likely reduce the job execution time?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job directly increases the parallelism of the Apache Spark-based execution environment. Since the job reads Parquet files, filters rows, and writes output, a bottleneck in compute capacity is the most likely cause of prolonged execution time. More DPUs allow Spark to distribute the workload across more executors, reducing overall runtime.

Answer

Use a single large file instead of multiple small files.

Answer

Reduce the number of partitions in the output data.

Answer

Convert the input files from Parquet to CSV format.

Question 9

Refer to the exhibit. A data engineer is troubleshooting a permissions issue. The IAM role 'DataEngineerRole' is used by an AWS Glue job that needs to read data from an S3 bucket encrypted with a customer managed KMS key. The above key policy is attached to the KMS key. The Glue job fails with an AccessDenied error when trying to read the data. What is the MOST likely cause?

Accepted Answer

The key policy condition 'kms:ViaService' restricts KMS actions to only when they are made through S3, but AWS Glue calls KMS directly, not via S3.. The key policy includes a condition `kms:ViaService` that restricts KMS actions to only when they are made through the S3 service. However, AWS Glue does not call KMS via S3; it calls KMS directly to decrypt the S3 object's data key. Because the Glue job's KMS request does not originate from the S3 service, the condition fails, resulting in an AccessDenied error.

Answer

The key policy requires requests to originate from a VPC endpoint, but the Glue job is not using one.

Answer

The key policy denies requests that are not using HTTPS, but the Glue job is using HTTPS.

Answer

The Glue job is running in a different AWS region than the S3 bucket.

Question 10

A company uses AWS DMS to migrate an on-premises Oracle database to Amazon Aurora PostgreSQL. The migration is ongoing with continuous replication. The data engineer notices that the target Aurora database has a higher lag than expected. Which action would most likely reduce the lag?

Accepted Answer

Increase the number of parallel tasks in the DMS task settings. Increasing the number of parallel tasks in a DMS task improves throughput, allowing data to be loaded faster to the target and reducing replication lag. Option A is incorrect because the S3 bucket size does not affect DMS replication performance. Option C is incorrect: while Batch Optimized Apply can reduce apply overhead on certain targets like PostgreSQL, increasing parallel tasks is a more direct and effective way to address lag. Option D is incorrect because disabling validation reduces data integrity checks and may provide only minor lag reduction, but it is not the recommended primary action.

Answer

Increase the size of the S3 bucket used for staging

Answer

Enable Batch Optimized Apply on the DMS task

Answer

Disable validation of data on the target

Question 11

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). AWS DMS supports continuous replication from PostgreSQL source databases using logical replication slots to capture CDC events in near real-time. It can directly stream these changes to Amazon S3 as a target, making it the correct choice for this use case.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 12

A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The replication is used for near-real-time analytics. Recently, the DMS task started failing with an error indicating insufficient memory. The source database is large (2 TB). What should a data engineer do to resolve this issue while minimizing changes to the existing architecture?

Accepted Answer

Increase the DMS replication instance size.. The error indicates the DMS replication instance is running out of memory during continuous replication of a 2 TB Oracle database to S3 in Parquet format. Increasing the replication instance size (Option D) directly addresses the memory constraint by providing more RAM and processing capacity, which is necessary for handling large volumes of Change Data Capture (CDC) data and Parquet conversion overhead. This solution requires minimal architectural changes, as it only involves modifying the instance class in the DMS task settings.

Answer

Change the target format to JSON to reduce memory usage.

Answer

Split the DMS task into multiple smaller tasks.

Answer

Use Change Data Capture (CDC) only, without full load.

Question 13

A company is running a 10-node Amazon EMR cluster to process data from Amazon S3. The cluster is using Apache Spark for transformations. The data processing is taking longer than expected. Which THREE actions can improve the performance of the Spark jobs on EMR? (Choose THREE.)

Accepted Answer

Enable dynamic allocation of executors.. Enabling dynamic allocation of executors allows Amazon EMR to automatically scale the number of executors up or down based on workload demand. This prevents resource underutilization or over-provisioning, which can significantly improve Spark job performance by ensuring that the cluster's resources are efficiently matched to the processing needs of the transformations.

Answer

Reduce the number of shuffle partitions.

Answer

Disable speculative execution to reduce redundant tasks.

Question 14

A data engineer needs to ingest data from an on-premises Oracle database to Amazon S3 daily. The data volume is 500 GB per day, and the network bandwidth is 200 Mbps. The requirement is to minimize the impact on the source database and ensure data integrity. Which combination of AWS services should be used?

Accepted Answer

AWS Database Migration Service (DMS) with S3 as target. AWS DMS with S3 as target is correct because it supports continuous change data capture (CDC) from Oracle, minimizing impact on the source database by reading redo logs instead of querying tables directly. It can handle 500 GB/day over 200 Mbps (which yields ~2.16 TB/day theoretical max) and ensures data integrity via transactional consistency and validation checksums. DMS also automatically partitions large datasets and can resume from failures, making it ideal for daily bulk loads.

Answer

AWS Glue ETL jobs with JDBC connection

Answer

Amazon Kinesis Data Firehose with Oracle as source

Answer

AWS Data Pipeline with SQLActivity

Question 15

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is JSON and each record is about 2 KB. The delivery stream is configured to buffer incoming data to 5 MB or 60 seconds, whichever comes first. The data engineering team notices that the S3 bucket contains many small files (average 2 MB), which makes subsequent processing inefficient. They need to reduce the number of small files without increasing the latency beyond 5 minutes. Which solution should they implement?

Accepted Answer

Increase the buffer size to 50 MB and the buffer interval to 300 seconds.. Increasing the buffer size to 50 MB and the buffer interval to 300 seconds directly addresses the root cause: the current 5 MB buffer size triggers a flush too frequently, producing many 2 MB files. By raising the buffer size to 50 MB, each flush will contain more data, resulting in larger S3 objects (up to ~50 MB uncompressed), while the 300-second interval ensures latency stays within the 5-minute requirement. This reduces the number of small files without requiring additional services or post-processing.

Answer

Enable compression (GZIP) on the delivery stream.

Answer

Use a Lambda function to merge small files after delivery.

Answer

Decrease the buffer size to 1 MB and the buffer interval to 60 seconds.

Question 16

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an Amazon S3 bucket. Recently, the application has been failing with 'ResourceNotFoundException' for the S3 bucket. What is the MOST likely cause?

Accepted Answer

The S3 bucket ARN is incorrectly specified in the application configuration.. The 'ResourceNotFoundException' for the S3 bucket indicates that the application cannot find the bucket. This is most likely due to an incorrectly specified bucket name or ARN in the application configuration. If the bucket name is misspelled or the ARN is malformed, the Kinesis Data Analytics application cannot resolve the bucket resource. Option A is incorrect because missing 's3:PutObject' permission would cause an 'AccessDenied' error, not 'ResourceNotFoundException'. Option C is incorrect because the AWS Region for S3 is determined by the bucket's location, and specifying a wrong region in Flink code would typically result in a different error (e.g., 'IllegalArgumentException' or cross-region access issues) but not 'ResourceNotFoundException'. Option D is incorrect because versioning is unrelated to bucket existence; disabling versioning does not cause a resource not found error.

Answer

The IAM role used by the application does not have s3:PutObject permission.

Answer

The Flink application code specifies the wrong AWS Region for the S3 bucket.

Answer

The S3 bucket has versioning disabled.

Question 17

A company uses Amazon DynamoDB for a gaming application that requires single-digit millisecond read and write latencies. The application experiences throttling on the 'GameScores' table during peak hours. The table has a partition key of 'game_id' and a sort key of 'player_id'. The data engineer needs to improve performance without changing the table's provisioned capacity. Which THREE actions should the engineer take? (Choose THREE.)

Accepted Answer

Enable DynamoDB auto scaling to adjust capacity based on traffic patterns.. DynamoDB auto scaling automatically adjusts the provisioned read and write capacity based on actual traffic patterns, preventing throttling during peak hours without manual intervention. This allows the table to handle bursts while maintaining single-digit millisecond latencies, as long as the traffic stays within the auto scaling limits.

Answer

Enable DynamoDB adaptive capacity to allow more throughput per partition.

Answer

Increase the read capacity units (RCUs) to twice the peak observed value.

Question 18

A data engineer is designing a data ingestion pipeline for real-time clickstream data from a website. The data must be stored in Amazon S3 in near-real time, and also be available for real-time analytics using Amazon Athena. The pipeline must handle occasional spikes of up to 10x the normal throughput. Which combination of services should the engineer use?

Accepted Answer

Amazon Kinesis Data Streams with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.. Amazon Kinesis Data Streams can handle high throughput spikes (10x normal) with its scalable stream capacity. AWS Lambda can process records in near-real time and write them to Amazon S3. Amazon Athena can query the data directly from S3 using its schema-on-read approach. Option A is wrong because Amazon SQS is a pull-based queue; Lambda would need to poll SQS, adding latency and complexity for near-real-time ingestion. Option B is wrong because AWS DMS is designed for database migration and continuous replication, not for ingesting real-time clickstream data from a website. Option D is wrong because Amazon Kinesis Data Firehose is a simpler option for streaming data to S3, but the question requires near-real-time availability for Athena; Firehose has a minimum buffer interval of 60 seconds, which may not meet near-real-time requirements, and AWS Glue is typically used for batch ETL rather than low-latency streaming transformations.

Answer

Amazon Simple Queue Service (SQS) with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.

Answer

AWS Database Migration Service (DMS) to stream data to Amazon S3, and Amazon Athena for queries.

Answer

Amazon Kinesis Data Firehose with AWS Glue for transformation, and Amazon Athena for queries.

Question 19

A company runs a data pipeline on Amazon EMR that processes terabytes of data daily. The pipeline reads from Amazon S3, performs transformations using Spark, and writes results back to S3. Recently, the data engineer noticed that the EMR cluster's spot instances are frequently reclaimed, causing job failures and delays. The cluster uses a mix of On-Demand and Spot instances. The engineer wants to minimize job interruptions while keeping costs low. The current configuration uses a single EMR cluster with a core node group of 10 On-Demand instances and a task node group of 20 Spot instances. The job failures occur during the shuffle phase when tasks on Spot instances are lost. The engineer has no control over when spot instances are reclaimed. Which action will MOST effectively reduce job failures while maintaining cost efficiency?

Accepted Answer

Enable EMR managed scaling to automatically add On-Demand instances when Spot instances are reclaimed.. Enabling EMR managed scaling allows the cluster to automatically add On-Demand instances when Spot instances are reclaimed, providing dynamic capacity to prevent job failures during the shuffle phase without significantly increasing costs. Option A is incorrect because increasing On-Demand instances in the core node group does not directly address the loss of Spot task nodes and can increase costs. Option B is incorrect because increasing the bid price to the On-Demand price does not guarantee avoidance of reclaims and still relies on Spot instances. Option C is incorrect because using only On-Demand instances would eliminate Spot savings and increase costs significantly.

Answer

Increase the number of On-Demand instances in the core node group to 20.

Answer

Configure the task node group to use only Spot instances and increase the bid price to the On-Demand price.

Answer

Change the task node group to use only On-Demand instances.

Question 20

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. AWS Transfer Family supports FTP, FTPS, and SFTP protocols and can be integrated with a VPC using Elastic IPs or a VPC endpoint, enabling secure access to an FTP server reachable only via VPN. It automates the transfer of files from the external FTP server to Amazon S3 on a schedule without requiring custom code or infrastructure management.

Answer

AWS Snowcone

Answer

AWS Glue with a Python shell

Answer

AWS DataSync