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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Orchestrate data pipelines
|
| Topic 2: Data Operations and Support | 22% | - Automate data pipelines
|
| Topic 3: Data Store Management | 26% | - Understand data cataloging
|
| Topic 4: Data Security and Governance | 18% | - Implement data quality checks
|
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NEW QUESTION # 194
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures.
References:
Amazon Managed Service for Apache Flink
Apache Flink
Window Aggregations in Flink
NEW QUESTION # 195
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: A
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 # 196
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?
A: Inspect the job run monitoring section of the AWS Glue console.
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions likeA-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.
NEW QUESTION # 197
An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs.
Which solution will meet these requirements?
Answer: A
Explanation:
Option C is correct because AWS Lambda's advanced logging controls support separate application log level and system log level filtering, but AWS documentation states that for Lambda to filter application logs according to their log level, the function must use JSON formatted logs. AWS also documents that in the advanced logging configuration you can choose a log level such as ERROR for application logs and WARN for system logs. Therefore, configuring the function to use JSON log format is the necessary step that enables the required log-level filtering behavior.
Option A is irrelevant because the function already writes logs successfully. Option B alone is insufficient because changing code-level logging does not configure Lambda's separate system log filtering behavior, and Lambda's application log filtering relies on structured JSON logs. Option D changes the log destination but does not implement the required filtering levels. The official Lambda documentation makes clear that JSON log format is the enabling configuration for this feature, so that is the correct answer.
NEW QUESTION # 198
A company runs concurrent analytical queries on Amazon Redshift tables multiple times each day. The queries require consistent data views three times each day. The company runs extract, transform, and load (ETL) operations that update dimension tables while the queries run. The company has noticed that the queries cause table-level locks during the ETL operations. The company ' s current solution experiences query timeouts and deadlocks during peak processing hours, which affects analytical reporting and on-demand analysis.
Which solution will fix this issue?
Answer: B
Explanation:
Option D is correct because the problem is lock contention between ETL writes and analytical reads on the same Redshift tables. AWS documents that table locks can block both reads and writes, and that Redshift data sharing lets you share live data across Redshift clusters and workgroups without manually moving or copying the data. Because the shared data is live, consumer users see the most up-to-date information as soon as it is updated. Separating ETL and analytics onto different Redshift clusters while using data sharing is therefore the cleanest way to isolate workloads and still provide consistent data views.
Option A reduces contention only by changing schedules and does not fully solve concurrent peak-hour conflicts. Option B is not correct because Redshift federated queries are read-only against external sources and do not provide a Redshift "read replica" design for this case. Option C does not address the locking problem on the dimension tables that ETL is updating. The official Redshift guidance and service capabilities point to workload isolation plus data sharing as the best fix. This also matches the exam guide's emphasis on choosing architectures that maintain performance and consistency without unnecessary copying.
NEW QUESTION # 199
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