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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Implement access control and authentication
- Enforce compliance and data governance
|
| Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
| Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Ensure reliability and scalability - Backup, restore, and disaster recovery |
| Data Ingestion and Transformation | 34% | - Transform and enrich data
- Ingest data from various sources
|
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NEW QUESTION # 63
A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?
Answer: A
Explanation:
* Problem Analysis:
* The pipeline processescompressed filesin S3 and must supportincremental data processing.
* AWS Glue features must facilitatetracking progressto avoid reprocessing the same data.
* Key Considerations:
* Incremental data processing requires tracking which files or partitions have already been processed.
* The solution must be automated and efficient for large-scale ETL jobs.
* Solution Analysis:
* Option A: Workflows
* Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.
* Option B: Triggers
* Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.
* Option C: Job Bookmarks
* Job bookmarks track the state of the data that has been processed, enabling incremental processing.
* Automatically skip files or partitions that were previously processed in Glue jobs.
* Option D: Classifiers
* Classifiers determine the schema of incoming data but do not handle incremental processing.
* Final Recommendation:
* Job bookmarksare specifically designed to enable incremental data processing in AWS Glue ETL pipelines.
:
AWS Glue Job Bookmarks Documentation
AWS Glue ETL Features
NEW QUESTION # 64
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: B,E
Explanation:
* Problem Analysis:
* The company usesKinesis Data Streamsfor both inventory management and reordering.
* TheKinesis Producer Library (KPL)publishes data, and theKinesis 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 asnetwork timeouts,shard splits, or changes inrecord processorscan 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 timeoutsandchanges in shards or processorsare 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 # 65
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: D
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 helpwith 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 .
1: AWS Data Exchange User Guide
2: AWS Data Exchange FAQs
3: AWS Glue Developer Guide
4: AWS CodeCommit User Guide
5: Amazon Kinesis Data Streams Developer Guide
6: Amazon Elastic Container Registry User Guide
7: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
NEW QUESTION # 66
A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years.
Answer: A
Explanation:
S3 Lifecycle policies automate data retention and deletion. By specifying an expiration rule for 7 years, objects older than that period are permanently deleted without manual intervention.
"To automatically delete aged data, configure an S3 Lifecycle rule with an expiration policy for objects older than the retention period."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 67
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?
Answer: D
Explanation:
To make the S3 data accessible daily in the AWS Glue Data Catalog, the data engineer needs to create a crawler that can crawl the S3 data and write the metadata to the Data Catalog. The crawler also needs to run on a daily schedule to keep the Data Catalog updated with the latest data. Therefore, the solution must include the following steps:
* Create an IAM role that has the necessary permissions to access the S3 data and the Data Catalog. The AWSGlueServiceRole policy is a managed policy that grants these permissions1.
* Associate the role with the crawler.
* Specify the S3 bucket path of the source data as the crawler's data store. The crawler will scan the data and infer the schema and format2.
* Create a daily schedule to run the crawler. The crawler will run at the specified time every day and update the Data Catalog with any changes in the data3.
* Specify a database name for the output. The crawler will create or update a table in the Data Catalog under the specified database. The table will contain the metadata about the data in the S3 bucket, such as the location, schema, and classification.
Option B is the only solution that includes all these steps. Therefore, option B is the correct answer.
Option A is incorrect because it configures the output destination to a new path in the existing S3 bucket. This is unnecessary and may cause confusion, as the crawler does not write any data to the S3 bucket, only metadata to the Data Catalog.
Option C is incorrect because it allocates data processing units (DPUs) to run the crawler every day. This is also unnecessary, as DPUs are only used for AWS Glue ETL jobs, not crawlers.
Option D is incorrect because it combines the errors of option A and C. It configures the output destination to a new path in the existing S3 bucket and allocates DPUs to run the crawler every day, both of which are irrelevant for the crawler.
:
1: AWS managed (predefined) policies for AWS Glue - AWS Glue
2: Data Catalog and crawlers in AWS Glue - AWS Glue
3: Scheduling an AWS Glue crawler - AWS Glue
[4]: Parameters set on Data Catalog tables by crawler - AWS Glue
[5]: AWS Glue pricing - Amazon Web Services (AWS)
NEW QUESTION # 68
......
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