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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Automate operational tasks - Monitor and troubleshoot data pipelines
- Ensure reliability and scalability |
| Topic 2: Data Store Management | 26% | - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Topic 3: Data Security and Governance | 18% | - Encrypt data at rest and in transit - Enforce compliance and data governance
- Implement access control and authentication
|
| Topic 4: Data Ingestion and Transformation | 34% | - Transform and enrich data
|
>> Data-Engineer-Associate Free Sample Questions <<
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NEW QUESTION # 267
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
B . Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
C . Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
D . Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
Reference:
1: AWS Data Exchange User Guide
2: AWS Data Exchange FAQs
3: AWS Glue Developer Guide
: AWS CodeCommit User Guide
: Amazon Kinesis Data Streams Developer Guide
: Amazon Elastic Container Registry User Guide
: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
NEW QUESTION # 268
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)
Answer: D,E
Explanation:
Problem Analysis:
The company uses Kinesis Data Streams for both inventory management and reordering.
The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.
Duplicate records were observed in the inventory reordering system.
Key Considerations:
Kinesis streams are designed for durability but may produce duplicates under certain conditions.
Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.
Solution Analysis:
Option A: Network-Related Timeouts
If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
Option B: High IteratorAgeMilliseconds
High iterator age suggests delays in processing but does not directly cause duplication.
Option C: Changes in Shards or Processors
Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
Option D: AggregationEnabled Set to True
AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
Option E: High max_records Value
A high max_records value increases batch size but does not lead to duplication.
Final Recommendation:
Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview
NEW QUESTION # 269
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:
Which solution will meet this requirement with the LEAST coding effort?
Answer: D
Explanation:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:
* 7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns)
* NEST TO MAP - AWS Glue DataBrew (Section: Syntax)
NEW QUESTION # 270
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon Redshift supportsdynamic data masking, which enables you to limit sensitive data visibility to specific users and roles without duplicating the data. This approach supports showing only parts of a column's values (e.g., last four digits) and full visibility for authorized roles (e.g., auditors).
"With dynamic data masking, you can control how much sensitive data a user sees in query results without changing the data in the table."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf IAM roles are used to associate users with the appropriate masking rules, keeping security tight and avoiding the creation of duplicate data views or tables.
NEW QUESTION # 271
A company runs an AWS Glue workflow every day to process time series data from an Amazon S3 bucket.
The workflow loads the data into an Amazon Redshift Serverless table. The company observes that some of the jobs in the workflow occasionally fail.
A data engineer must receive a notification when the Redshift table does not contain the most recent data.
Which solution will meet this requirement in the MOST operationally efficient way?
Answer: B
Explanation:
Option B is the most operationally efficient because it checks the business requirement directly: whether the target table contains the most recent data, not merely whether a job failed. Monitoring only failures (Options C and D) can produce false positives (a job failure might not impact freshness) and false negatives (a job can succeed but still load stale or incomplete data). The study material emphasizes implementing data quality validation as part of the ETL process so data can be verified before or as it is stored, rather than relying only on pipeline execution status.
Using a data quality rule focused on freshness (for example, validating that a "max event timestamp" or
"latest partition date" meets today's expected value) lets the pipeline detect stale loads even when the workflow runs. Then, an EventBridge rule can route failures of that data quality check to SNS for immediate notification, keeping operations serverless and centralized. Macie (Option A) is designed for sensitive-data discovery/classification, not operational "freshness" checks on Redshift tables, so it adds unnecessary services and effort compared to a Glue-native data quality validation approach.
NEW QUESTION # 272
......
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