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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Choose a data store
|
| Data Operations and Support | 22% | - Monitor data pipelines
|
| Data Security and Governance | 18% | - Manage data privacy and compliance
|
| Data Ingestion and Transformation | 34% | - Transform and process data
|
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NEW QUESTION # 190
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes.
The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:
Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3) Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes) Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section: Modifying the volume type)
NEW QUESTION # 191
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?
Answer: D
Explanation:
To ensure that Branch B is up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform a git rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch.
git rebase master:
This command moves the commits of Branch B to be based on top of the latest state of the master branch. It allows the developer to resolve any conflicts and create a clean history.
Reference:
Alternatives Considered:
A (git diff): This will only show differences between Branch B and master but won't resolve conflicts or bring Branch B up to date.
B (git pull master): Pulling the master branch directly does not offer the same clean history management as rebase.
D (git fetch -b): This is an incorrect command.
Git Rebase Best Practices
NEW QUESTION # 192
A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.
The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.
Which solution will meet these requirements?
Answer: C
Explanation:
AWS Glue DataBrew provides a no-code data preparation and validation interface. It allows you to set data profiling, completeness checks, and numeric range validations directly through its UI-ideal for IoT validation use cases.
"AWS Glue DataBrew enables users to define validation rules such as completeness and value range checks without writing code."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This fulfills the requirement for minimal coding and automatic data quality flagging.
NEW QUESTION # 193
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?
Answer: C
Explanation:
Problem Analysis:
The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.
Data updates must propagate quickly to OpenSearch for analytics or search purposes.
Key Considerations:
DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).
Integration with AWS Lambda allows seamless processing of these changes.
OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.
Solution Analysis:
Option A: Step Functions with Periodic Export
Not suitable for near-real-time updates; introduces significant latency.
Operationally complex to manage periodic exports and S3 data ingestion.
Option B: AWS Glue Job
AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.
Option C: DynamoDB Streams + Lambda
DynamoDB Streams capture changes in near real time.
Lambda can process these streams and use the OpenSearch API to update the index.
This approach provides low latency and seamless integration with minimal operational overhead.
Option D: Custom OpenSearch Plugin
Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.
Implementation Steps:
Enable DynamoDB Streams for the relevant DynamoDB tables.
Create a Lambda function to process stream records:
Parse insert, update, and delete events.
Use OpenSearch APIs to index or update documents based on the event type.
Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.
Monitor and log errors for debugging and operational health.
Reference:
Amazon DynamoDB Streams Documentation
AWS Lambda and DynamoDB Integration
Amazon OpenSearch Service APIs
NEW QUESTION # 194
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Select TWO.)
Answer: B,E
Explanation:
Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.
* Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.
* Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.
Other options:
* SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.
* Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.
References:
* Amazon Neptune Documentation
* Gremlin Documentation
* SPARQL Documentation
NEW QUESTION # 195
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