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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
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NEW QUESTION # 72
A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?
Answer: A
Explanation:
* Problem Analysis:
* The company needs to migrate both anapplicationand anon-premises Apache Kafka serverto AWS.
* Incremental updates from an on-premises Oracle database are processed by Kafka.
* The solution must follow areplatform migration strategy, prioritizing minimal changes andlow management overhead.
* Key Considerations:
* Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.
* The solution must provide amanaged Kafka serviceto minimize operational burden.
* Low overhead solutions like serverless services are preferred.
* Solution Analysis:
* Option A: Kinesis Data Streams
* Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.
* This option would require significant application refactoring, which does not align with the replatform strategy.
* Option B: MSK Provisioned Cluster
* Managed Kafka service with fully configurable clusters.
* Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.
* Option C: Amazon Kinesis Data Firehose
* Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.
* Not suitable for Kafka-based applications.
* Option D: MSK Serverless
* MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.
* Automatically scales based on workload, reducing operational overhead.
* Ideal for replatform migrations, as it requires minimal changes to the application.
* Final Recommendation:
* Amazon MSK Serverlessis the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.
:
Amazon MSK Serverless Overview
Comparison of Amazon MSK and Kinesis
NEW QUESTION # 73
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. After a merger, a query joining two large sales tables becomes slow. Table S1 has 10 billion records, Table S2 has 900 million records.
The query performance must improve.
Answer: B,E
Explanation:
To optimize joins between large tables, Redshift recommends using KEY distribution on a common, high- cardinality join key to ensure co-location of data blocks. The Amazon Redshift Advisor identifies performance bottlenecks and provides recommendations for distribution and sort keys.
"Use KEY distribution when joining large tables on a common high-cardinality column. Use Redshift Advisor to identify query optimization opportunities."
NEW QUESTION # 74
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: C
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 # 75
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?
Answer: D
Explanation:
Problem Analysis:
The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.
If a file is incomplete or empty, the previous day's file should be retained.
Need a solution to validate files before overwriting the existing file.
Key Considerations:
Automate data validation with minimal human intervention.
Use built-in AWS Glue capabilities for ease of integration.
Ensure robust validation for missing or incomplete data.
Solution Analysis:
Option A: Lambda Function for Validation
Lambda can validate files, but it would require custom code.
Does not leverage AWS Glue's built-in features, adding operational complexity.
Option B: AWS Glue Data Quality Rules
AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.
Rules can validate if required fields are missing or if the file is empty.
Automatically integrates into the existing ETL pipeline.
If validation fails, retain the previous day's file.
Option C: AWS Glue Studio with Filling Missing Values
Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.
Does not handle empty files effectively.
Option D: Athena Query for Validation
Athena can drop rows with missing values, but this is a post-hoc solution.
Requires manual intervention to copy the corrected file to S3, increasing complexity.
Final Recommendation:
Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.
This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.
Implementation Steps:
Enable AWS Glue Data Quality in the existing ETL pipeline.
Define DQDL Rules, such as:
Check if a file is empty.
Verify required fields are present and non-null.
Configure the pipeline to proceed with overwriting only if the file passes validation.
In case of failure, retain the previous day's file.
AWS Glue Data Quality Overview
Defining DQDL Rules
AWS Glue Studio Documentation
NEW QUESTION # 76
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:
Option A is the best answer because AWS DMS documentation recommends that, when practical, you create the replication instance in the same Region as your target endpoint, and in the same VPC or subnet as your target endpoint. In this scenario, the target is the Amazon Redshift cluster in eu-west-1 in Account_B, so placing the replication instance there is the most appropriate design. AWS DMS uses the replication instance to connect to the source, read the source data, transform it as needed, and load it into the target.
This also makes architectural sense because the replication instance must have network connectivity to both endpoints, and colocating it with the Redshift target usually simplifies connectivity to the target warehouse and aligns with AWS guidance. A replication instance in Account_B, eu-west-1 can still connect to the PostgreSQL source in Account_A, eu-east-1, provided the required networking and permissions are configured. AWS DMS supports replication tasks by defining a replication instance plus source and target endpoints.
The study guide also identifies AWS DMS as the correct managed service for database migration and continuous replication scenarios, including full load and change data capture. That matches this cross-account, cross-Region migration use case.
NEW QUESTION # 77
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