DEA-C01 Practice Exam 3 — 20 Questions

Question 1

An e-commerce application uses Amazon ElastiCache for Redis to cache product catalog data. The cache currently uses lazy loading. The team wants to ensure that frequently accessed product data is always fresh. Which caching strategy should they implement?

Accepted Answer

Write-through caching. Write-through caching ensures that data is written to the cache simultaneously with the database, guaranteeing that frequently accessed product data is always fresh. This strategy eliminates stale reads by synchronously updating the cache on every write, which directly addresses the requirement for freshness without relying on expiration or lazy population.

Answer

Set a TTL of 5 minutes for all cached items

Answer

Use database read replicas to serve data

Answer

Lazy loading with TTL

Question 2

A company runs a multi-AZ Amazon RDS for PostgreSQL instance. They need to run a one-time analytical query that will take several hours and consume significant I/O. The query should not impact the primary workload. What should the data engineer do?

Accepted Answer

Create a read replica of the RDS instance and run the query on the replica.. Option A is correct because creating a read replica of the RDS for PostgreSQL instance allows the analytical query to run on a separate database engine without affecting the primary workload. Read replicas in Amazon RDS use asynchronous replication from the source instance, so the replica can handle heavy I/O and long-running queries independently. This ensures the primary instance remains available for the production workload without performance degradation.

Answer

Run the query directly on the primary instance during off-peak hours.

Answer

Increase the instance size to handle the load.

Answer

Enable Multi-AZ and run the query on the standby instance.

Question 3

A data engineer is troubleshooting an AWS Lake Formation permissions issue. A user is able to query an Amazon Athena table but cannot see the underlying S3 data in the AWS Glue Data Catalog. The user has been granted SELECT permission on the table in Lake Formation. What is the most likely cause?

Accepted Answer

The user does not have DESCRIBE permission on the table in Lake Formation.. In AWS Lake Formation, the ability to query a table via Athena (which requires SELECT permission) is separate from the ability to view the table's metadata in the Glue Data Catalog. To see the underlying S3 data location or table properties in the catalog, a user needs DESCRIBE permission on the table. Without DESCRIBE, the table appears invisible in the Glue console or API, even though SELECT queries succeed.

Answer

The data location is not registered with Lake Formation.

Answer

The S3 bucket policy does not grant the user access.

Answer

The user does not have the aws:SourceArn condition in the IAM policy.

Question 4

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 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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

Question 20

Match each AWS Glue component to its role.