DEA-C01 Practice Test 26 — 15 Questions

Question 1

Refer to the exhibit. A data engineer reviews an Amazon S3 server access log entry for an object upload. The log shows a status of 200 and encryption status "AES256". The company policy requires that all data be encrypted with SSE-KMS. Which action should the engineer take to enforce this policy?

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

Accepted Answer

Attach an S3 bucket policy that denies s3:PutObject unless the request includes x-amz-server-side-encryption: aws:kms. The log shows the object was uploaded with SSE-S3 (AES256), not the required SSE-KMS. To enforce the policy, the engineer should attach an S3 bucket policy that denies s3:PutObject unless the request includes the x-amz-server-side-encryption header set to aws:kms. Option D (default encryption) would encrypt new objects with SSE-KMS, but it does not block uploads that explicitly use SSE-S3; default encryption only applies when no encryption header is specified. Options B and C do not enforce the policy: revoking IAM permissions is too broad and CloudTrail only logs events.

Answer

Revoke the IAM role's s3:PutObject permission

Answer

Enable AWS CloudTrail data events to monitor future uploads

Answer

Enable S3 default encryption with SSE-KMS on the bucket

Question 2

A data engineer needs to migrate an on-premises MySQL database to Amazon RDS for MySQL with minimal downtime. Which approach should they use?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication from the source database.. AWS DMS with ongoing replication (change data capture, CDC) is the correct approach because it allows continuous synchronization from the on-premises MySQL source to the RDS target, enabling a cutover with minimal downtime. Unlike one-time export/import tools, DMS captures ongoing changes during the migration, so the target stays up-to-date until you switch over.

Answer

Use mysqldump to export the database and import into RDS.

Answer

Create an RDS read replica and promote it.

Answer

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

Question 3

A company uses Amazon DynamoDB to store metadata for a document management system. The table has a partition key of document_id and a sort key of version. The application frequently queries for the latest version of a document by document_id. The data engineer notices that these queries are consuming a lot of read capacity. How can the engineer optimize the read performance and reduce read capacity consumption?

Accepted Answer

Create a global secondary index (GSI) and use DynamoDB Accelerator (DAX).. Creating a Global Secondary Index (GSI) with document_id as the partition key and version as the sort key, combined with DynamoDB Accelerator (DAX), allows the application to query the latest version efficiently. The GSI can be configured to project only the necessary attributes, reducing read capacity consumption, while DAX provides an in-memory cache that offloads repeated read requests from the DynamoDB table, significantly lowering read capacity units (RCUs) consumed.

Answer

Change the sort key to store version in descending order.

Answer

Enable DynamoDB Streams and use a read replica.

Answer

Decrease the ReadCapacityUnits of the table to force caching.

Question 4

A data engineer needs to store semi-structured JSON data that is accessed infrequently but must be retrievable within minutes when needed. The data will be stored for 7 years for compliance. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is the most cost-effective storage class for data that is accessed infrequently, requires retrieval within minutes (using expedited retrieval), and must be stored for 7 years for compliance. It offers the lowest storage cost among S3 classes, making it ideal for long-term archival of semi-structured JSON data with minimal access needs.

Answer

Amazon S3 One Zone-Infrequent Access

Answer

Amazon S3 Standard

Answer

Amazon S3 Intelligent-Tiering

Question 5

A data engineer is designing a data ingestion pipeline to load data from an on-premises Oracle database to Amazon S3. The pipeline should capture changes in near real-time (within minutes) and minimize impact on the source database. The source table has a 'last_modified' timestamp column. Which service combination would meet these requirements?

Accepted Answer

AWS DMS with a replication task in CDC mode, writing to S3 in Parquet format.. AWS DMS with a replication task in CDC (Change Data Capture) mode is the correct choice because it continuously reads the Oracle redo logs to capture near real-time changes (within seconds to minutes) with minimal impact on the source database. It can directly write to S3 in Parquet format, meeting the requirement for low-latency ingestion without full table scans.

Answer

Amazon Kinesis Data Firehose with a Lambda function that queries Oracle.

Answer

AWS Data Pipeline with a periodic SQL query activity to copy full table snapshots.

Answer

AWS Glue with a JDBC connection to Oracle, running a crawler every 5 minutes.

Question 6

A company is using an Amazon RDS for MySQL database for an e-commerce application. During a sales event, the database experiences high read traffic, causing slow query performance. The company wants to reduce the read load on the primary database without changing the application code. Which solution meets these requirements?

Accepted Answer

Create an Amazon RDS read replica and direct read traffic to it.. An Amazon RDS read replica is a read-only copy of the primary database that can offload read traffic without requiring any application code changes. By directing read queries to the replica, the primary database's load is reduced, improving performance during high-read events. This solution is specifically designed for read-heavy workloads and integrates seamlessly with existing MySQL connections.

Answer

Enable Multi-AZ on the RDS instance.

Answer

Increase the instance size of the RDS database.

Answer

Deploy Amazon ElastiCache to cache query results.

Question 7

A data engineer is migrating an on-premises Apache Hive data warehouse to Amazon EMR. The warehouse contains partitioned tables stored in HDFS. The engineer wants to use Amazon S3 as the storage layer for the EMR cluster. What is the MOST important consideration for maintaining query performance on S3?

Accepted Answer

Ensure that the table partitions are organized in a way that minimizes S3 LIST requests. When using Amazon S3 as the storage layer for an EMR cluster, the most critical factor for query performance is minimizing S3 LIST requests. S3 LIST operations are significantly slower and more expensive than GET requests, and Hive/Spark queries on partitioned tables often issue LIST requests to discover partition locations. By organizing partitions with a common prefix (e.g., `year=2023/month=01/day=15/`) and using partition pruning, you reduce the number of LIST calls, directly improving query latency and reducing S3 API costs.

Answer

Configure EMR to use HDFS for storage instead of S3 for better performance

Answer

Use DynamoDB as the Hive metastore to improve metadata access

Answer

Use Amazon Redshift Spectrum to query the data directly from S3

Question 8

A company uses Amazon Redshift for analytics. A data engineer notices that queries are slow due to high disk usage on the compute nodes. The engineer needs to reclaim disk space without interrupting ongoing queries. Which action should the engineer take?

Accepted Answer

Run VACUUM DELETE to reclaim space from deleted rows. VACUUM DELETE specifically reclaims disk space from deleted rows without requiring an exclusive table lock, allowing ongoing queries to continue. In Amazon Redshift, deleted rows consume disk space until reclaimed, and VACUUM DELETE operates in the background to free that space while maintaining query concurrency.

Answer

Use the COPY command to reload data

Answer

Run VACUUM FULL on all tables

Answer

Resize the cluster to a larger instance type

Question 9

A company has an Amazon Redshift cluster that stores petabytes of data. Queries are experiencing high disk usage due to large intermediate results. The data engineer needs to improve query performance without adding more nodes. Which action should the engineer take?

Accepted Answer

Set appropriate distribution keys to minimize data movement.. Setting appropriate distribution keys can minimize data movement during joins and aggregations, which directly reduces the size of intermediate results spilled to disk. This improves query performance without adding nodes. Option C (compression) reduces storage size and I/O but does not directly address disk usage from large intermediate results. Option B (WLM) limits concurrency, not disk usage. Option D (sort keys) improves scan efficiency, not intermediate result size.

Answer

Configure workload management (WLM) queues to limit concurrency.

Answer

Apply column compression encoding to reduce data size.

Answer

Define sort keys on all columns used in WHERE clauses.

Question 10

A company uses Amazon Redshift for analytics. They notice that some queries are slow due to data redistribution. The data engineer wants to minimize data movement across nodes. Which table design strategy should be used? (Choose TWO.)

Accepted Answer

Choose a distribution key that matches the join key for large tables.. When large tables are joined on their distribution keys, Redshift can perform a collocated join, meaning the matching rows are already on the same node slice, eliminating the need to redistribute data across the network. This directly minimizes data movement and speeds up query execution.

Answer

Set the distribution style to AUTO for all tables.

Answer

Define compound sort keys on frequently filtered columns.

Answer

Use EVEN distribution for all tables.

Question 11

A data engineer needs to store time-series sensor data from thousands of IoT devices. The data is written once, read frequently for the last 24 hours, and rarely accessed after 30 days. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon Timestream with a 30-day retention policy.. Amazon Timestream is purpose-built for time-series data, offering automatic storage tiering where recent data resides in memory for fast queries and historical data is moved to a cost-optimized magnetic store. A 30-day retention policy aligns perfectly with the requirement to keep data accessible for frequent reads over the last 24 hours while automatically expiring older data, minimizing storage costs without manual intervention.

Answer

Amazon Redshift with automatic compression and distribution keys.

Answer

Amazon DynamoDB with TTL to expire data after 30 days.

Answer

Amazon S3 with lifecycle policies to transition to S3 Glacier after 30 days.

Question 12

A company uses Amazon DynamoDB to store session data for a web application. The application experiences sudden spikes in traffic, causing occasional throttling errors. The data engineer needs to handle these spikes without over-provisioning capacity. What is the MOST cost-effective solution?

Accepted Answer

Configure DynamoDB Auto Scaling to automatically adjust provisioned capacity.. DynamoDB Auto Scaling (option C) is the most cost-effective solution because it automatically adjusts the provisioned read/write capacity based on actual traffic patterns, handling sudden spikes without manual intervention or over-provisioning. This avoids paying for unused capacity during low-traffic periods while still accommodating bursts within the configured limits.

Answer

Set up a TTL (Time to Live) attribute to automatically delete old session data.

Answer

Enable DynamoDB Accelerator (DAX) to cache read requests.

Answer

Switch to DynamoDB on-demand mode.

Question 13

A data engineer needs to ingest daily CSV files from an external FTP server into Amazon S3. The files are 5 GB each. Which service is MOST suitable to automate this ingestion?

Accepted Answer

AWS DataSync. AWS DataSync is the most suitable service for automated, scheduled transfers of large files from an external FTP server to Amazon S3. It supports both one-time and recurring transfers and can handle high throughput for files up to 5 GB. S3 Transfer Acceleration only speeds up uploads to S3, not transfers from FTP servers. AWS Glue is an ETL service, not a file transfer tool. AWS AppSync is for real-time APIs, not batch file ingestion.

Answer

AWS AppSync

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Glue

Question 14

Refer to the exhibit. A data engineer is troubleshooting a Kinesis Data Streams consumer that is falling behind. The stream has 2 shards and is receiving data at a rate of 2 MB/s. The consumer is an AWS Lambda function with a batch size of 100 records. What should the engineer do to improve consumer throughput?

Accepted Answer

Increase the number of shards in the stream to 4. Increasing the number of shards to 4 doubles the stream's total ingestion capacity to 4 MB/s, which directly increases the number of concurrent Lambda invocations and thus consumer throughput. With 2 shards, each shard can support up to 1 MB/s input and 2 MB/s output, so the current 2 MB/s load is at the shard-level output limit, causing the consumer to fall behind.

Answer

Decrease the Lambda batch size to 10 records

Answer

Increase the retention period of the stream to 168 hours

Answer

Increase the memory allocation of the Lambda function

Question 15

Refer to the exhibit. A data engineer is using a Kinesis Data Stream with one shard. The application writes 2000 records per second, each 1 KB. The put record calls are frequently throttled. What is the most likely cause?

Accepted Answer

The stream has only one shard, which limits writes to 1000 records per second. A Kinesis Data Stream shard has a write throughput limit of 1,000 records per second (or 1 MB per second). Since the application is writing 2,000 records per second (each 1 KB) to a single shard, it exceeds the shard's record-per-second quota, causing the PutRecord calls to be throttled. The solution is to increase the number of shards to at least two to distribute the load.

Answer

The retention period of 24 hours is too short

Answer

The stream uses KMS encryption, causing additional latency

Answer

Enhanced monitoring is not enabled, causing performance issues