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| Section | Weight | Objectives |
|---|---|---|
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
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NEW QUESTION # 111
A company runs a data pipeline that uses AWS Step Functions to orchestrate AWS Lambda functions and AWS Glue jobs. The Lambda functions and AWS Glue jobs require access to multiple Amazon RDS databases. The Lambda functions and AWS Glue jobs already have access to the VPC that hosts the RDS databases.
Which solution will meet these requirements in the MOST secure way?
Answer: A
Explanation:
AWS security best practices require avoiding long-term credentials and enforcing the principle of least privilege. Using IAM roles with role assumption is the most secure approach for granting temporary, scoped access to AWS resources.
In this solution, an IAM role is created with permissions to access the Amazon RDS databases. A second IAM role is assigned to the AWS Lambda functions and AWS Glue jobs with permission to assume the RDS- access role. This approach eliminates hard-coded credentials, enables automatic credential rotation through AWS Security Token Service (AWS STS), and provides clear separation of duties.
Using the root user or long-term access keys is explicitly discouraged by AWS. Creating IAM users for services violates best practices, as AWS services should use IAM roles, not users. Embedding credentials in JDBC connection strings exposes sensitive information and increases the risk of credential leakage.
Therefore, using IAM role assumption provides the strongest security posture, auditability, and compliance with AWS Well-Architected Framework guidance.
NEW QUESTION # 112
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places.
The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?
Answer: C
NEW QUESTION # 113
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: D
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 # 114
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift.
The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?
Answer: C
Explanation:
The issue described is that the AWS Glue job is reprocessing files from previous runs despite the bookmark feature being enabled. Bookmarks in AWS Glue allow jobs to keep track of which files or data have already been processed to avoid reprocessing. The most likely reason for reprocessing the files is missing S3 permissions, specifically s3
* s3
is a permission required by AWS Glue when bookmarks are enabled to ensure Glue can retrieve metadata from the files in S3, which is necessary for the bookmark mechanism to function correctly. Without this permission, Glue cannot track which files have been processed, resulting in reprocessing during subsequent runs.
* Concurrency settings (Option B) and the version of AWS Glue (Option C) do not affect the bookmark behavior. Similarly, the lack of a commit statement (Option D) is not applicable in this context, as Glue handles commits internally when interacting with Redshift and S3.
Thus, the root cause is likely related to insufficient permissions on the S3 bucket, specifically s3
, which is required for bookmarks to work as expected.
References:
* AWS Glue Job Bookmarks Documentation
* AWS Glue Permissions for Bookmarks
NEW QUESTION # 115
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?
Answer: D
Explanation:
Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient.
Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code. References:
AWS Lambda layers
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda
NEW QUESTION # 116
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