DEA-C01 Practice Questions 2 — 50 Questions

Question 1

A data engineer is designing a data lake on Amazon S3 with AWS Lake Formation. The data lake contains personally identifiable information (PII). The company has a policy that only users who have completed data privacy training can access the PII data. The training status is stored in an external identity provider (IdP) as an attribute. The data engineer needs to enforce this policy using Lake Formation. Which THREE steps should the data engineer take? (Choose THREE.)

Accepted Answer

Create an LF-tag called 'trainingCompleted' with values 'true' and 'false'. Grant 'SELECT' permission on the LF-tag 'trainingCompleted=true' to the federated users.. Option A is correct because LF-tags allow Lake Formation to manage access based on metadata attributes. By creating an LF-tag 'trainingCompleted' with values 'true' and 'false', and granting SELECT permission on the tag value 'true' to federated users, the data engineer can enforce that only users with the training attribute can access the tagged resources. This approach decouples access control from IAM roles and leverages tag-based authorization, which is the recommended method for attribute-based access control (ABAC) in Lake Formation.

Answer

Create a column-level filter on the PII columns that limits access based on the user's training attribute.

Answer

Create an IAM role for each user and attach a policy that allows 'lakeformation:GetDataAccess' only if the user has the training attribute.

Question 2

Refer to the exhibit. A data engineer runs the above AWS CLI command to view the table metadata in the AWS Glue Data Catalog. The data is stored as CSV in S3 with partitions by year and month. When querying the table using Amazon Athena, no data is returned. What is the most likely cause?

Accepted Answer

The partitions have not been added to the Glue Data Catalog.. Option A is correct because the AWS CLI command shown only retrieves table metadata, not partition metadata. In AWS Glue, partitions must be explicitly added to the Data Catalog via `MSCK REPAIR TABLE`, `ALTER TABLE ADD PARTITION`, or a Glue crawler. Without partition metadata, Athena cannot locate the data files under the partitioned S3 paths (e.g., `s3://bucket/year=2024/month=01/`), resulting in zero rows returned even though the table schema is defined.

Answer

The SerDe is not compatible with CSV files.

Answer

The S3 location points to a file instead of a folder.

Answer

The column data types are incorrect for the CSV data.

Question 3

A data engineer needs to ingest JSON data from an on-premises relational database into Amazon S3 every hour. Which AWS service should be used to set up a scheduled, incremental data transfer?

Accepted Answer

AWS Database Migration Service (DMS) with S3 as target.. AWS DMS is purpose-built for migrating databases to AWS targets, including Amazon S3. It supports ongoing replication (change data capture) and scheduled full-load tasks, making it ideal for hourly incremental transfers from an on-premises relational database to S3 without custom scripting.

Answer

Amazon S3 Transfer Acceleration with a cron job.

Answer

AWS Glue with a JDBC connection and a scheduled crawler.

Answer

Amazon Kinesis Data Firehose with a database source.

Question 4

A company is using AWS Glue to process data from Amazon S3. The Glue job reads CSV files and writes Parquet files to a different S3 bucket. The job occasionally fails with 'java.lang.OutOfMemoryError: Java heap space'. The data size varies. Which change should the engineer make to avoid this error?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. The 'java.lang.OutOfMemoryError: Java heap space' error in AWS Glue indicates that the Spark executors ran out of memory while processing the data. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory available across the cluster, allowing larger datasets to be processed without hitting the heap limit. Each DPU provides 4 vCPUs and 16 GB of memory, so adding more DPUs scales memory linearly.

Answer

Convert the CSV files to JSON format before processing.

Answer

Decrease the Spark shuffle partitions in the job script.

Answer

Increase the job timeout setting.

Question 5

A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms each record and writes it to Amazon S3. Recently, the Lambda function started failing with 'ProvisionedThroughputExceededException' errors when writing to S3. The team has already increased the Lambda function's memory and timeout. Which action should the team take to resolve the issue?

Accepted Answer

Implement retries with exponential backoff in the Lambda function for S3 put operations.. The 'ProvisionedThroughputExceededException' error indicates that the Lambda function is being throttled by S3 due to exceeding the bucket's request rate limits. Implementing retries with exponential backoff in the Lambda function for S3 put operations is the correct solution because it allows the function to gracefully handle transient throttling errors by waiting progressively longer between retries, which aligns with AWS's guidance for managing S3 request rate limits.

Answer

Use S3 Batch Operations to write data in batches.

Answer

Increase the number of shards in the Kinesis data stream.

Answer

Enable S3 Transfer Acceleration on the destination bucket.

Question 6

A company uses AWS Glue to perform ETL on data stored in Amazon S3. The Glue job reads CSV files, converts them to Parquet, and partitions by date. The job runs daily and processes about 500 GB of data. The team wants to optimize costs and performance. Which three actions should the team take? (Select THREE.)

Accepted Answer

Use column pruning to read only necessary columns in the Glue script.. Option B is correct because column pruning in AWS Glue scripts reduces the amount of data read from Amazon S3 by specifying only the columns needed for the ETL transformation. This minimizes I/O and network overhead, directly lowering costs and improving job performance, especially when processing large CSV files.

Answer

Increase the Spark shuffle partitions to 500.

Answer

Write the output as JSON instead of Parquet to avoid compression overhead.

Question 7

A company uses Amazon DynamoDB for a gaming application. They need to store player session data that expires after 24 hours. Which DynamoDB feature should they use to automatically delete expired items?

Accepted Answer

Time to Live (TTL). DynamoDB Time to Live (TTL) is the correct feature because it allows you to define a per-item timestamp attribute (e.g., `expireAt`) that DynamoDB automatically deletes once that timestamp is reached. This is ideal for expiring session data after 24 hours without requiring custom code or scheduled jobs to scan and delete items, reducing cost and operational overhead.

Answer

DynamoDB auto scaling

Answer

DynamoDB Streams

Answer

Point-in-time recovery

Question 8

A company stores time-series sensor data in Amazon S3. They need to query the data using SQL with minimal latency and no infrastructure management. Which service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any infrastructure to manage. It is optimized for querying structured, semi-structured, and unstructured data stored in S3, making it ideal for time-series sensor data with minimal latency requirements.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 9

Match each AWS service to its primary purpose in data engineering.

Question 10

Match each AWS Glue component to its role.

Question 11

Match each AWS data compression format to its typical use case.

Question 12

Match each AWS storage class to its description.

Question 13

Match each AWS monitoring tool to its primary use.

Question 14

A company uses Amazon DynamoDB as a source for an AWS Glue job. The job reads a large table using a DynamoDB export to S3 feature. The job is failing with 'ThrottlingException' from DynamoDB. What should the data engineer do to resolve this issue WITHOUT changing the job's logic?

Accepted Answer

Use the DynamoDB export to S3 feature and read the exported data from S3. Option C is correct because the DynamoDB export to S3 feature creates a point-in-time snapshot of the table data in S3 without consuming any read capacity units (RCUs) from the DynamoDB table. By reading the exported data from S3 instead of directly scanning the DynamoDB table, the Glue job avoids triggering ThrottlingException entirely, as the export operation uses the table's backup and restore mechanism, not the read path. This resolves the issue without altering the job's logic, as the job can be reconfigured to read from the S3 export location.

Answer

Use DynamoDB Streams to capture changes and process them incrementally

Answer

Reduce the number of DynamoDB read segments in the Glue job

Answer

Increase the read capacity units (RCU) of the DynamoDB table

Question 15

A company has an Amazon RDS for MySQL DB instance with read replicas. The primary DB instance fails. What is the correct procedure to promote a read replica to become the new primary?

Accepted Answer

Manually promote the read replica to a standalone DB instance.. When an Amazon RDS for MySQL primary DB instance fails, read replicas do not automatically become the new primary. The correct procedure is to manually promote the read replica using the AWS Management Console, CLI, or API, which converts it into a standalone DB instance. After promotion, you must update your application endpoints to point to the new primary, as RDS does not handle this automatically.

Answer

Modify the read replica to be a Multi-AZ deployment and failover will occur.

Answer

RDS automatically fails over to the read replica within 5 minutes.

Answer

Delete the primary and the read replica will automatically become the primary.

Question 16

A data engineer has set up an Amazon S3 lifecycle policy to transition objects to Glacier Instant Retrieval after 30 days. After 60 days, objects should transition to Deep Archive. However, objects are not transitioning to Deep Archive. What is the most likely cause?

Accepted Answer

The transition to Deep Archive requires a minimum of 30 days after the previous transition.. Amazon S3 lifecycle policies require a minimum of 30 days between successive transitions when moving objects from one storage class to another. Since the policy transitions objects to Glacier Instant Retrieval after 30 days, the subsequent transition to Deep Archive cannot occur until at least 60 days after creation (30 days for the first transition plus 30 days minimum gap). The current policy attempts the Deep Archive transition at 60 days, which is exactly 30 days after the first transition, meeting the minimum requirement. However, the most likely cause of the failure is that the policy is incorrectly configured or the objects are too new; given the options, the correct answer is that the transition to Deep Archive requires a minimum of 30 days after the previous transition, and the policy as described should work if the objects are older than 60 days, but the question implies a timing issue.

Answer

The bucket has versioning enabled.

Answer

Deep Archive is not supported in the bucket's region.

Answer

Objects are smaller than 128 KB.

Question 17

A company is migrating its on-premises MySQL database to Amazon RDS for MySQL. They want to minimize downtime and ensure data consistency. Which AWS service should be used for the migration?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it is specifically designed for migrating databases to AWS with minimal downtime. DMS supports homogeneous migrations like MySQL to Amazon RDS for MySQL, and it uses ongoing replication (change data capture) to keep the source and target databases in sync during the migration, ensuring data consistency and allowing a cutover with only seconds of downtime.

Answer

AWS S3 Transfer Acceleration

Answer

AWS Glue

Answer

AWS Snowball Edge

Question 18

A data engineer is monitoring an Amazon Kinesis Data Analytics application that uses a SQL query to aggregate streaming data. The application is falling behind and the millisBehindLatest metric is increasing. Which action should the engineer take to improve performance?

Accepted Answer

Increase the Parallelism setting of the Kinesis Data Analytics application. Increasing the Parallelism setting of the Kinesis Data Analytics application allows the SQL query to process data across more in-application streams and operators concurrently, directly addressing the lag indicated by the rising millisBehindLatest metric. This action scales the compute resources allocated to the application without changing the source stream or the query logic, making it the most direct way to improve throughput for a SQL-based Kinesis Data Analytics application.

Answer

Switch from SQL to Apache Flink for the analytics application

Answer

Increase the number of shards in the source Kinesis stream

Answer

Decrease the window duration of the SQL query

Question 19

A company uses Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'game_id' and a sort key of 'score'. The read capacity is provisioned at 1000 RCUs. During peak hours, users report high latency when querying the top 10 scores for a specific game. The DynamoDB metrics show ConsumedReadCapacityUnits averaging 800 but occasional throttling. What is the most likely cause and solution?

Accepted Answer

The hot game_id partition is exceeding its throughput; add DynamoDB Accelerator (DAX) to cache reads. The hot game_id partition is exceeding its provisioned throughput because DynamoDB distributes RCUs evenly across partitions, and a single partition can only handle up to (1000 RCUs / number of partitions) per second. When a specific game_id becomes popular, all reads hit the same partition, causing throttling despite low overall consumed capacity. Adding DynamoDB Accelerator (DAX) caches the top 10 scores for that partition, reducing read pressure and eliminating throttling without increasing RCUs.

Answer

Create a global secondary index with the same key schema to distribute reads

Answer

Remove the sort key and use a global secondary index

Answer

Increase the provisioned RCUs to 2000

Question 20

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a custom consumer application that writes to Amazon S3 every 5 minutes. The consumer is falling behind and processing lag is increasing. Which action is MOST effective to reduce the lag?

Accepted Answer

Increase the number of shards in the Kinesis stream. The consumer is falling behind because the stream's throughput capacity is insufficient for the incoming data volume. Increasing the number of shards in the Kinesis stream directly increases the total read capacity (each shard provides 2 MB/s read throughput and 5 transactions/second), allowing the consumer to process more data in parallel and reduce lag.

Answer

Switch to Amazon Kinesis Data Firehose to deliver data directly to S3

Answer

Increase the batch size of records written to S3

Answer

Reduce the retention period of the stream