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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| 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 Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
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NEW QUESTION # 210
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: B
NEW QUESTION # 211
A data engineer is building a new data pipeline that stores metadata in an Amazon DynamoDB table. The data engineer must ensure that all items that are older than a specified age are removed from the DynamoDB table daily.
Which solution will meet this requirement with the LEAST configuration effort?
Answer: A
Explanation:
DynamoDB Time to Live (TTL) automatically expires and deletes items based on a timestamp attribute - it requires minimal setup and runs without additional infrastructure.
The engineer only needs to define a TTL attribute (e.g., expiration_time) and set its epoch value when items are inserted.
"To automatically delete old items from a DynamoDB table, enable TTL and define a timestamp attribute.
Items are removed automatically after the specified time with no extra maintenance."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This is serverless, free, and requires no scheduling or code-based cleanup.
NEW QUESTION # 212
A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?
Answer: B
Explanation:
Problem Analysis:
The company needs near real-time replication of MySQL updates to Amazon Redshift.
Minimal development effort is required for this solution.
Key Considerations:
AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.
DMS integrates natively with both MySQL and Redshift, simplifying setup.
Solution Analysis:
Option A: AWS Glue Job
Glue is batch-oriented and does not support near real-time replication.
Option B: DMS with Full Load + CDC
Efficiently handles initial database load and continuous updates.
Requires minimal setup and operational overhead.
Option C: AppFlow SDK
AppFlow is not designed for database replication. Custom connectors increase development effort.
Option D: DataSync
DataSync is for file synchronization and not suitable for database updates.
Final Recommendation:
Use AWS DMS in full load + CDC mode for continuous replication.
AWS Database Migration Service Documentation
Setting Up DMS with Redshift
NEW QUESTION # 213
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?
Answer: D
Explanation:
The data engineer needs to orchestrate ETL jobs that include Spark jobs on Amazon EMR, API calls to Salesforce, and loading data into Redshift. They also need automatic failure handling and retries. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the best solution for this requirement.
* Option A: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)Apache Airflow is designed for complex job orchestration, allowing users to define workflows (DAGs) in Python. MWAA manages Airflow and its integrations with other AWS services, including Amazon EMR, Redshift, and external APIs like Salesforce. It provides automatic retry handling, failure detection, and detailed monitoring, which fits the use case perfectly.
* Option B (AWS Step Functions) can orchestrate tasks but doesn't natively support complex workflow definitions with Python like Airflow does.
* Option C (AWS Glue) is more focused on ETL and doesn't handle the orchestration of external systems like Salesforce as well as Airflow.
* Option D (Amazon EventBridge) is more suited for event-driven architectures rather than complex workflow orchestration.
References:
* Amazon Managed Workflows for Apache Airflow
* Apache Airflow on AWS
NEW QUESTION # 214
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 # 215
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