DEA-C01 Practice Test 20 — 15 Questions

Question 1

A company uses Amazon Redshift for its data warehouse. The cluster has multiple node types and is configured with automated snapshots. The company needs to ensure high availability and disaster recovery across AWS Regions. Which THREE actions should the company take to meet these requirements? (Choose THREE.)

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

Accepted Answer

Restore a snapshot from the secondary Region in the event of a disaster.. Restoring a snapshot from a secondary Region is the core disaster recovery action in a cross-Region DR strategy. When a primary Region fails, you can restore the cross-Region snapshot copy to a new Redshift cluster in the secondary Region, ensuring business continuity. This leverages the automated cross-Region copy configured in Option E to make the snapshot available in the DR Region.

Answer

Enable automated snapshots with a retention period of at least 1 day.

Answer

Create manual snapshots on a daily basis and copy them to another Region.

Question 2

A company uses Kinesis Data Analytics for SQL-based real-time analytics on streaming data. They notice that the application is processing data slower than the incoming rate, causing increased latency. Which action is MOST likely to improve the throughput?

Accepted Answer

Increase the number of Kinesis Processing Units (KPUs) for the application. Kinesis Data Analytics for SQL applications processes data using Kinesis Processing Units (KPUs), which define the compute and memory resources available. When the incoming data rate exceeds the processing capacity, increasing the number of KPUs directly scales the application's parallelism and throughput, allowing it to keep up with the stream. This is the most direct way to reduce latency caused by insufficient processing power.

Answer

Increase the number of shards in the Kinesis data stream

Answer

Enable auto-scaling on the Kinesis data stream

Answer

Decrease the retention period of the Kinesis data stream

Question 3

A data engineer is designing a data lake on Amazon S3. The data includes sensitive customer information that must be encrypted at rest. Which combination of actions meets this requirement with minimal operational overhead?

Accepted Answer

Enable default encryption on the S3 bucket using SSE-S3. SSE-S3 provides server-side encryption with Amazon S3-managed keys, which encrypts data at rest with minimal operational overhead because AWS handles key management, rotation, and encryption/decryption transparently. Enabling default encryption on the S3 bucket ensures that all objects written to the bucket are automatically encrypted without requiring any client-side changes or additional code, meeting the requirement with the least administrative effort.

Answer

Encrypt objects client-side before uploading

Answer

Use an S3 bucket policy to deny writes without encryption

Answer

Use an S3 Lifecycle policy to transition to Glacier

Question 4

Match each AWS database service to its primary use case.

Accepted Answer

Amazon RDS: Relational database for online transaction processing (OLTP) workloads.. Correct matches: RDS -> OLTP relational, DynamoDB -> NoSQL low-latency, Redshift -> data warehousing, ElastiCache -> in-memory caching. Common confusions include swapping RDS and DynamoDB use cases.

Answer

Amazon RDS: NoSQL key-value database for low-latency applications.

Answer

Amazon DynamoDB: Relational database for OLTP workloads.

Question 5

An organization needs to ingest data from on-premises databases into AWS S3 for archival purposes. The data volume is several TB per day, and the network has moderate bandwidth. Which AWS service is BEST suited for this bulk data transfer?

Accepted Answer

AWS DataSync. AWS DataSync is the best choice because it is designed for efficiently moving large volumes of data (several TB per day) from on-premises storage to AWS, including S3. It automates and accelerates data transfer over the existing network using a purpose-built protocol and parallel multi-threading, while handling encryption, validation, and scheduling. For moderate bandwidth, DataSync optimizes throughput and can resume interrupted transfers, making it ideal for ongoing archival ingestion.

Answer

AWS Direct Connect

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball

Question 6

A data engineer is troubleshooting an Amazon RDS for MySQL instance that is experiencing high read latency. The instance is a Single-AZ db.r5.large with 100 GB of General Purpose (gp2) storage. Which action is most likely to reduce read latency?

Accepted Answer

Create a read replica and direct read queries to it.. Creating a read replica offloads SELECT queries from the primary instance, directly reducing the read load and thus read latency. Since the instance is Single-AZ and experiencing high read latency, a read replica distributes the read traffic without altering the existing storage or availability configuration.

Answer

Enable automatic backups with a 7-day retention.

Answer

Increase the allocated storage to 200 GB.

Answer

Convert the instance to a Multi-AZ deployment.

Question 7

A data engineer is designing a data store for a real-time analytics application that requires sub-millisecond read and write latency. The data is accessed via a REST API. Which AWS services should the engineer consider? (Choose THREE.)

Accepted Answer

Amazon ElastiCache for Redis. Amazon ElastiCache for Redis is correct because it provides an in-memory data store that delivers sub-millisecond read and write latency, ideal for real-time analytics. Redis supports data structures like strings, hashes, and sorted sets, and can be accessed via REST API through a caching layer or directly with Redis commands. This makes it suitable for low-latency, high-throughput workloads where disk-based storage would introduce unacceptable delays.

Answer

Amazon S3

Answer

Amazon RDS for MySQL

Question 8

Refer to the exhibit. A data engineer creates an AWS Glue job using this CloudFormation template. The job processes new data files in S3 and uses job bookmarks to track processed files. After initial success, the job runs again but processes all files again instead of only new ones. What is the most likely cause?

Accepted Answer

The S3 input path does not have a partitioning scheme or timestamp to identify new files. AWS Glue job bookmarks rely on the structure of the input data to identify new files. Without a partitioning scheme or a timestamp-based naming convention in the S3 path, Glue cannot determine which files are new; it falls back to reprocessing all files. The job bookmark feature tracks processed files by examining the S3 path and file metadata, so a flat or non-partitioned structure prevents it from distinguishing new files from old ones.

Answer

The job bookmark option is set to 'job-bookmark-disable'

Answer

The enable-metrics parameter is set to true

Answer

The MaxRetries parameter is set to 0

Question 9

A company stores sensitive data in an Amazon S3 bucket. To comply with regulations, all data must be encrypted at rest using server-side encryption. The security team wants to ensure that any attempt to upload an unencrypted object is automatically denied. Which S3 bucket policy condition should be used?

Accepted Answer

s3:x-amz-server-side-encryption. The s3:x-amz-server-side-encryption condition key enforces that objects must be encrypted with AES-256 (SSE-S3). s3:x-amz-server-side-encryption-aws-kms-key-id is for KMS key enforcement. s3:x-amz-acl controls access control lists, not encryption.

Answer

s3:x-amz-server-side-encryption-aws-kms-key-id

Answer

s3:x-amz-acl

Answer

s3:x-amz-storage-class

Question 10

A social media company ingests user activity data from multiple sources into Amazon S3. The data is in JSON format and includes fields: user_id, activity_type, timestamp, and metadata. The company wants to transform this data into a columnar format (Parquet) partitioned by date and activity_type for efficient querying with Amazon Athena. The pipeline must handle data that arrives up to 3 days late. Currently, a daily AWS Glue ETL job scans the entire S3 bucket for new files, transforms them, and writes to a separate output bucket. The job is taking longer as data volume grows, and the team wants to reduce processing time and cost. What should the engineer do?

Accepted Answer

Set up S3 event notifications to invoke an AWS Lambda function that triggers a Glue job for each new object, passing the object key so the job processes only that file.. Using S3 event notifications with Lambda to trigger a Glue job for each new file allows incremental processing, reducing the time and cost of scanning the entire S3 bucket. Option A (increasing DPUs) does not address the root cause of scanning all files. Option B (partition projection) helps with query performance but not with the transformation process. Option C (replacing Glue with EMR) adds operational overhead and is not necessary for this use case.

Answer

Increase the number of DPUs for the Glue job to process data faster.

Answer

Use AWS Glue partition projection and schema inference to reduce scan time.

Answer

Replace AWS Glue with Amazon EMR and use Spark to process data in parallel.

Question 11

A company uses a Kinesis Data Firehose delivery stream to load data into an S3 bucket. The data is in JSON format and must be converted to Parquet before landing in S3. Which steps are required to achieve this? (Choose THREE.)

Accepted Answer

Configure the Firehose delivery stream to enable data format conversion to Parquet.. Kinesis Data Firehose natively supports converting incoming data from JSON to Parquet format. This conversion is enabled directly in the delivery stream configuration, eliminating the need for separate processing steps.

Answer

Store the schema in Amazon DynamoDB.

Answer

Use Kinesis Data Analytics to convert the data.

Question 12

A company needs to ingest data from an on-premises SQL Server database into Amazon Redshift. The data volume is less than 1 TB and the network bandwidth is limited. Which AWS service should be used for the initial full load?

Accepted Answer

AWS Database Migration Service (DMS). AWS DMS is designed for migrating databases to AWS, including to Redshift. Option A (AWS Snowball) is for large data volumes (petabytes) and not efficient for <1 TB. Option C (Amazon S3 Transfer Acceleration) speeds up uploads to S3 but not directly to Redshift. Option D (AWS Direct Connect) is a network connection, not a migration service.

Answer

AWS Snowball Edge

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Direct Connect

Question 13

A data engineer is responsible for a data warehouse on Amazon Redshift that stores 5 TB of data. The engineer needs to load 50 GB of new data daily from Amazon S3 into Redshift. The current load process uses the COPY command and takes 2 hours, which is within the maintenance window. However, the engineer wants to optimize the load time and reduce the impact on concurrent queries. The engineer notices that the tables are not distributed evenly across the slices. The cluster has 4 nodes of dc2.large. Which approach will best improve load performance?

Accepted Answer

Change the distribution style of the tables to EVEN.. The COPY command distributes data across slices based on the table's distribution style. With dc2.large nodes, each node has 2 slices, so a 4-node cluster has 8 slices. If tables are not distributed evenly, some slices handle more data, causing bottlenecks. Changing the distribution style to EVEN forces rows to be spread uniformly across all slices, maximizing parallelism during the COPY load and reducing load time.

Answer

Increase the cluster size to 8 nodes.

Answer

Use GZIP compression on the S3 files.

Answer

Add sort keys to the tables based on the load timestamp.

Question 14

A company runs a nightly ETL job using AWS Glue. The job reads data from a JDBC connection to an on-premises MySQL database. The job fails with an error indicating that the connection pool is exhausted. What is the most likely cause and solution?

Accepted Answer

The Glue job is using too many executors, exhausting the database connections. Reduce the number of DPUs or increase the database max connections.. AWS Glue jobs distribute work across multiple executors, each of which opens its own JDBC connection to the source database. When the number of executors (controlled by DPUs) exceeds the database's configured maximum connections, the database connection pool is exhausted, causing the error. Reducing the number of DPUs or increasing the database's max_connections setting resolves the issue.

Answer

The database is not reachable due to network issues. Check VPC and security groups.

Answer

The Glue job is hitting the AWS Glue connection pool limit. Increase the Glue connection pool size.

Answer

The database credentials are expired. Rotate the password in AWS Secrets Manager.

Question 15

A company wants to ingest real-time clickstream data from a website into Amazon S3 with minimal code. The data should be delivered within 60 seconds of generation. Which AWS service should be used?

Accepted Answer

Amazon Kinesis Data Firehose. (Amazon Kinesis Data Firehose) is correct because it is a fully managed service that can ingest real-time clickstream data and deliver it to Amazon S3 in near-real-time (typically less than 60 seconds). Option B (AWS DMS) is for database migration, not streaming ingestion. Option C (Amazon Kinesis Data Streams) requires custom consumers to process and deliver data to S3, adding code overhead. Option D (S3 Transfer Acceleration) only speeds up uploads but does not provide streaming ingestion capabilities.

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Kinesis Data Streams

Answer

Amazon S3 Transfer Acceleration