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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 2: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Topic 3: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 4: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
>> Data-Engineer-Associate Musterprüfungsfragen <<
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86. Frage
A food delivery company manages thousands of deliveries simultaneously. Each delivery vehicle transmits real-time telemetry data as JSON events. The company wants to accelerate downstream analytics and simplify data processing. The company needs to flatten the telemetry data and then store the data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST latency?
Antwort: B
Begründung:
The correct answer is B because AWS Glue streaming ETL jobs are built to continuously consume data from streaming sources such as Amazon Kinesis Data Streams, transform the data, and load the results into Amazon S3 data lakes. The requirement is not just delivery to S3; the telemetry JSON must be flattened before storage, and this needs to happen with low latency. Amazon Data Firehose can deliver streaming data, but the option incorrectly assumes automatic flattening without a transformation layer.
SQS plus scheduled batch Glue introduces batch delay and does not meet the lowest-latency requirement. Athena is a query engine, not a real-time transformation and ingestion service for writing flattened streaming output to S3. AWS Glue streaming ETL is the correct serverless streaming transformation layer.
87. Frage
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?
Antwort: D
Begründung:
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.
Kinesis Data Streams Enhanced Fan-Out
PutRecords API for Batch Writes
88. Frage
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications.
The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)
Antwort: C,D
Begründung:
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job.
Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:
* Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)
* Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section:
Development tools)
* Monitoring with AWS Glue job run insights (Section: Requirements)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)
89. Frage
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)
Antwort: A,D
Begründung:
AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.
The other options are not optimal for the following reasons:
A: Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.
C: Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.
E: Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.
References:
1: AWS Lake Formation
2: AWS Lake Formation Permissions
3: AWS Identity and Access Management
4: Amazon S3
90. Frage
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)
Antwort: A,D
Begründung:
A data mesh is an architectural framework that organizes data into domains and treats data as products that are owned and offered for consumption by different teams1. A data mesh requires a centralized layer for data governance and access control, as well as a distributed layer for data storage and analysis. AWS Glue can provide data catalogs and ETL operations for the data mesh, but it cannot provide data governance and access control by itself2. Therefore, the company needs to use another AWS service for this purpose. AWS Lake Formation is a service that allows you to create, secure, and manage data lakes on AWS3. It integrates with AWS Glue and other AWS services to provide centralized data governance and access control for the data mesh. Therefore, option E is correct.
For data storage and analysis, the company can choose from different AWS services depending on their needs and preferences. However, one of the benefits of a data mesh is that it enables data to be stored and processed in a decoupled and scalable way1. Therefore, using serverless or managed services that can handle large volumes and varieties of data is preferable. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any type of data. Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Therefore, option B is a good choice for data storage and analysis in a data mesh. Option A, C, and D are not optimal because they either use relational databases that are not suitable for storing diverse and unstructured data, or they require more management and provisioning than serverless services. Reference:
1: What is a Data Mesh? - Data Mesh Architecture Explained - AWS
2: AWS Glue - Developer Guide
3: AWS Lake Formation - Features
[4]: Design a data mesh architecture using AWS Lake Formation and AWS Glue
[5]: Amazon S3 - Features
[6]: Amazon Athena - Features
91. Frage
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