DEA-C01 Practice Test 3 — 25 Questions

Question 1

A data engineer is troubleshooting an AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job runs successfully but writes duplicate rows into Redshift. The source data is static and does not contain duplicates. Which configuration change is most likely to resolve this issue?

Accepted Answer

Modify the job to use the 'postactions' option with a SQL statement that deletes duplicates before final insert.. The job runs successfully but writes duplicate rows because AWS Glue's Spark-based ETL jobs can retry tasks on failure, and when writing to Redshift using the JDBC connector, the default behavior is to append data without deduplication. Using the 'postactions' option with a SQL DELETE statement that removes duplicates before the final INSERT ensures that only unique rows remain, resolving the duplication without altering the source data.

Answer

Enable the 'upsert' feature in the Redshift connection by setting 'update' to true.

Answer

Use partition pruning on the S3 source to reduce the number of files read.

Answer

Increase the number of DPUs (Data Processing Units) allocated to the job.

Question 2

A data engineer is setting up an Amazon Redshift cluster for a data warehouse. The cluster will store historical sales data and support complex analytical queries. To optimize query performance and manage storage, the engineer needs to choose appropriate distribution styles and sort keys for a large fact table 'sales' and several dimension tables. Which TWO of the following design decisions are BEST practices?

Accepted Answer

Use interleaved sort keys on columns that are frequently used in filter predicates (e.g., date, region, product).. Option A is correct because interleaved sort keys in Amazon Redshift give equal weight to each column in the sort key, making them ideal for queries with filter predicates on multiple columns (e.g., date, region, product). This design optimizes zone maps and minimizes the amount of data scanned, significantly improving query performance for complex analytical workloads on large fact tables.

Answer

Use ALL distribution for the 'sales' fact table to replicate data to every node and avoid data movement.

Answer

Use a compound sort key with the most frequently filtered column first.

Answer

Choose AUTO distribution style for all tables and let Amazon Redshift automatically assign distribution.

Question 3

A data engineering team uses Amazon Redshift for analytics. They notice that queries on a large fact table are slow. The table is distributed using DISTSTYLE ALL. Which design change would most likely improve query performance?

Accepted Answer

Change DISTSTYLE to KEY on a column used in frequent joins.. DISTSTYLE ALL copies the entire table to every node, which is inefficient for large fact tables because it wastes storage and network bandwidth during data loading and query execution. Changing to DISTSTYLE KEY on a column used in frequent joins collocates related rows on the same slice, reducing the need to broadcast or redistribute data across the network during joins, which directly improves query performance.

Answer

Change DISTSTYLE to EVEN to distribute rows evenly across slices.

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Change the table to use a SORTKEY on the most frequently filtered column.

Question 4

An AWS Glue job that processes streaming data from Amazon Kinesis Data Streams is failing intermittently with 'Failed to checkpoint' errors. The job uses checkpointing to an Amazon S3 bucket every 60 seconds. Which action should the engineer take to resolve the issue?

Accepted Answer

Increase the checkpoint interval to 120 seconds.. The 'Failed to checkpoint' error in AWS Glue streaming jobs typically occurs when the checkpoint operation exceeds the 60-second interval due to high throughput or large state size. Increasing the checkpoint interval to 120 seconds provides more time for the checkpoint to complete, reducing the likelihood of timeouts and allowing the job to stabilize without losing progress.

Answer

Move the checkpoint location to an Amazon DynamoDB table.

Answer

Decrease the Kinesis shard count to reduce throughput.

Answer

Disable checkpointing and rely on Kinesis iterator age.

Question 5

Which TWO actions should a data engineer take to encrypt data at rest in an Amazon S3 bucket? (Select TWO.)

Accepted Answer

Configure the bucket to use SSE-KMS.. Option C is correct because SSE-KMS (Server-Side Encryption with AWS Key Management Service) encrypts data at rest in S3 by using a KMS key to manage encryption keys. This provides envelope encryption, where a CMK generates a data key that encrypts the object, and the data key is then encrypted by the CMK. Option D is correct because enabling default encryption on an S3 bucket using SSE-S3 (AES-256) ensures that all objects uploaded without explicit encryption headers are automatically encrypted at rest by S3's managed key.

Answer

Enable S3 Transfer Acceleration on the bucket.

Answer

Use client-side encryption before uploading objects to S3.

Answer

Attach a bucket policy that denies unencrypted PUT requests.

Question 6

A data engineer is configuring AWS Glue jobs to access data stored in Amazon S3. The data is encrypted using server-side encryption with AWS KMS (SSE-KMS). The Glue job needs to read and write data to the S3 bucket. Which IAM policy statement should be added to the Glue job's IAM role to allow it to use the KMS key?

Accepted Answer

{"Effect":"Allow","Action":["kms:Decrypt","kms:GenerateDataKey"],"Resource":"*"}. To read and write data encrypted with SSE-KMS, AWS Glue needs both `kms:Decrypt` (to read existing encrypted data) and `kms:GenerateDataKey` (to create a new data key for writing encrypted data). `kms:GenerateDataKey` is required because S3 uses a data key to encrypt objects, and the caller must generate that key via KMS. Option B correctly includes both actions, allowing the Glue job to perform read and write operations on the SSE-KMS encrypted bucket.

Answer

{"Effect":"Allow","Action":["kms:Decrypt"],"Resource":"*"}

Answer

{"Effect":"Allow","Action":["kms:Decrypt","kms:ReEncrypt"],"Resource":"*"}

Answer

{"Effect":"Allow","Action":["kms:Decrypt","kms:Encrypt"],"Resource":"*"}

Question 7

A company is building a data pipeline that ingests sensitive customer data from an on-premises database into Amazon S3 using AWS DMS. The data must be encrypted at rest in S3 and in transit. The security team requires that the encryption keys be managed by the company (not AWS). Which TWO actions should the data engineer take to meet these requirements? (Choose TWO.)

Accepted Answer

Configure the S3 bucket to use server-side encryption with AWS KMS (SSE-KMS) using a customer managed key.. Option B is correct because SSE-KMS with a customer managed key allows the company to control the encryption keys used for S3 server-side encryption, meeting the requirement that keys be managed by the company, not AWS. Option D is correct because enabling SSL/TLS on both the DMS source and target endpoints ensures data is encrypted in transit between the on-premises database and AWS DMS, and between DMS and S3, satisfying the in-transit encryption requirement.

Answer

Enable encryption at rest using the default DMS encryption settings.

Answer

Configure the S3 bucket to use server-side encryption with S3 managed keys (SSE-S3).

Answer

Create an AWS KMS key and use it in the DMS endpoint to encrypt data in transit.

Question 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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 16

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

Accepted Answer

Snappy: Designed for high speed and efficient compression, ideal for real-time data processing.. In AWS data engineering, compression format choice depends on trade-offs between compression ratio and speed. Snappy and LZO are optimized for speed, Gzip and Bzip2 for higher compression (with Bzip2 highest), and Zstandard offers a middle ground. Common misassociations include confusing Gzip with Bzip2 for highest compression and missing Snappy's speed focus.

Answer

Gzip: Provides the highest compression ratio, best for archival storage.

Answer

Bzip2: Commonly used for log files due to good compression ratio.

Question 17

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 18

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 19

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 20

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