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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Topic 2: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 3: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Topic 4: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
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NEW QUESTION # 221
A company needs to use an AWS Glue PySpark job to read specific data from an Amazon DynamoDB table.
The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data.
Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Amazon DynamoDB is optimized for Query operations, which retrieve items efficiently using the partition key and optionally the sort key. Queries consume significantly fewer RCUs than Scan operations because only matching partitions are read.
By performing a Query using the partition key in the key condition expression, the AWS Glue job reads only the required items directly from DynamoDB. This minimizes the number of read capacity units consumed and avoids unnecessary reads.
Using a Scan operation, even when followed by filtering in a DynamicFrame, consumes RCUs for the entire table or index and is highly inefficient. Using only the sort key in a query is invalid without the partition key and would not meet DynamoDB query requirements. The Glue DynamoDB connector filter option still performs a scan under the hood and does not minimize RCUs.
The queried data can be directly loaded into a DynamicFrame, satisfying the existing processing logic requirement without additional transformations.
Therefore, Option D provides the most efficient and cost-effective solution.
NEW QUESTION # 222
A company generates reports from 30 tables in an Amazon Redshift data warehouse. The data source is an operational Amazon Aurora MySQL database that contains 100 tables. Currently, the company refreshes all data from Aurora to Redshift every hour, which causes delays in report generation.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: B,E
Explanation:
* Option A (AWS DMS): Lets you replicate only selected tables from Aurora to Redshift. It supports ongoing replication (CDC) and reduces unnecessary data transfers.
* Option C (Zero-ETL with Aurora to Redshift): This new integration allows real-time, serverless, and low-maintenance data replication. It's designed to reduce operational overhead drastically.
Other options introduce manual processing or inefficient query access (Option D). Glue workflows (Option E) add unnecessary complexity and lag for near-real-time needs.
"Zero-ETL integration between Amazon Aurora and Redshift enables real-time analytics without building complex pipelines." Reference: AWS Blog - Introducing Amazon Aurora zero-ETL integration with Redshift
"With AWS DMS, you can replicate only selected tables from your database and use ongoing replication to reduce refresh latency."Reference: AWS DMS Documentation - AWS DMS Use Cases
NEW QUESTION # 223
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: C
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.
Reference:
AWS Glue Data Quality Overview
Defining DQDL Rules
AWS Glue Studio Documentation
NEW QUESTION # 224
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: C
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 # 225
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?
Answer: B
Explanation:
The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provideobject-level tracking for S3to capture detailed API actions on specific S3 objects, including the user who performed the action.
* AWS CloudTrail:
* CloudTrail can monitorAPI callsmade to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.
* CloudTrail logs include details such as theuser identity, which is essential for meeting the requirement of including the username in notifications.
* The CloudTrail logs can be forwarded toAmazon CloudWatchto trigger alarms based on certain access patterns (e.g., violations of specific policies).
Reference:Monitoring Amazon S3 Activity Using AWS CloudTrail
Amazon CloudWatch:
By forwarding CloudTrail logs to CloudWatch, you can set upalarmsthat are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.
Alternatives Considered:
A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.
B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.
D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API- level granularity like CloudTrail.
References:
AWS CloudTrail Integration with S3
Amazon CloudWatch Alarms
NEW QUESTION # 226
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