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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
| Topic 2: Data Operations and Support | 22% | - Automate operational tasks - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery |
| Topic 3: Data Ingestion and Transformation | 34% | - Ingest data from various sources
|
| Topic 4: Data Security and Governance | 18% | - Protect sensitive data - Encrypt data at rest and in transit - Enforce compliance and data governance
|
>> Data-Engineer-Associate Exam Questions <<
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NEW QUESTION # 274
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: A
Explanation:
The company needs to load data warehouse tables into Amazon S3 and perform incremental synchronization with daily updates. The most efficient solution is to use AWS Database Migration Service (AWS DMS) with a combination of full load and change data capture (CDC) to handle the initial load and daily incremental updates.
* Option A: Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.DMS is designed to migrate databases to AWS, and the combination of full load plus CDC is ideal for handling incremental data changes efficiently. AWS Glue can then be used to append the incremental data to the full data set in S3. This solution is highly operationally efficient because it automates both the full load and incremental updates.
Options B, C, and D are less operationally efficient because they either require writing custom logic to handle bookmarks manually or involve unnecessary daily full loads.
References:
* AWS Database Migration Service Documentation
* AWS Glue Documentation
NEW QUESTION # 275
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.
Which solution will meet these requirements?
Answer: D
Explanation:
Amazon Athena Federated Query enables SQL queries across multiple data sources without moving data.
Athena can directly query Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 using data source connectors. This capability is ideal for one-time or ad hoc analytical queries, where setting up ingestion pipelines would introduce unnecessary cost and delay.
Because the requirement is for a one-time performance report, the solution should minimize operational overhead and data duplication. Athena Federated Query executes queries in place, eliminating ETL processes, data exports, and staging layers. This significantly reduces query preparation time and avoids storage costs associated with copying data into a centralized location.
The other options require extensive data movement and setup. DynamoDB Streams, AWS DMS, Glue ETL pipelines, and Amazon EMR clusters are appropriate for ongoing analytics pipelines, not for a single analytical report. These approaches increase operational complexity and execution time.
Athena Federated Query is purpose-built for this exact scenario and provides the fastest path to insight with minimal overhead.
NEW QUESTION # 276
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?
Answer: D
Explanation:
To migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region (Account_A) to an Amazon Redshift cluster in the eu-west-1 Region (Account_B), AWS DMS needs a replication instance located in the target region (in this case, eu-west-1) to facilitate the data transfer between regions.
* Option A: Set up an AWS DMS replication instance in Account_B in eu-west-1.Placing the DMS replication instance in the target account and region (Account_B in eu-west-1) is the most efficient solution. The replication instance can connect to the source RDS PostgreSQL in eu-east-1 and migrate the data to the Redshift cluster in eu-west-1. This setup ensures data is replicated across AWS accounts and regions.
Options B, C, and D place the replication instance in either the wrong account or region, which increases complexity without adding any benefit.
References:
* AWS Database Migration Service (DMS) Documentation
* Cross-Region and Cross-Account Replication
NEW QUESTION # 277
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?
Answer: B
Explanation:
AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. Reference:
AWS Lake Formation
Row-Level Security
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation
NEW QUESTION # 278
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?
Answer: A
Explanation:
Problem Analysis:
Input Requirements: Gaming app generates approximately 20 KB data records, which must be ingested and made available to three internal consumers with dedicated throughput.
Key Requirements:
High throughput for ingestion from each device.
Dedicated processing bandwidth for each consumer.
Key Considerations:
Amazon Kinesis Data Streams supports high-throughput ingestion with PutRecords API for batch writes.
The Enhanced Fan-Out feature provides dedicated throughput to each consumer, avoiding bandwidth contention.
This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.
Solution Analysis:
Option A: Kinesis Data Streams + Enhanced Fan-Out
PutRecords API is designed for batch writes, improving ingestion performance.
Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.
Option B: Data Firehose + Dedicated Throughput Request
Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.
Option C: Data Firehose + Enhanced Fan-Out
Firehose does not support enhanced fan-out. This option is invalid.
Option D: Kinesis Data Streams + EC2 Instances
Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.
Final Recommendation:
Use Kinesis Data Streams with Enhanced Fan-Out for high-throughput ingestion and dedicated consumer bandwidth.
Reference:
Kinesis Data Streams Enhanced Fan-Out
PutRecords API for Batch Writes
NEW QUESTION # 279
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