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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q44-Q49):
NEW QUESTION # 44
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: D
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.
Reference:
AWS Glue Job Bookmarks Documentation
AWS Glue Permissions for Bookmarks
NEW QUESTION # 45
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: A
Explanation:
* Problem Analysis:
* The company runs adaily AWS Glue ETL pipelineto clean and transform files received in an S3 bucket.
* If a file isincomplete 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 definingData 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:
* UseAWS Glue Data Qualityto 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:
* EnableAWS Glue Data Qualityin the existing ETL pipeline.
* DefineDQDL 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 # 46
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
Answer: C
Explanation:
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake FormationFindMatchesis designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.
* D. Train and use the AWS Lake Formation FindMatches transform in the ETL job:
* FindMatchesis a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier.
* It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks.
* FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.
Reference:AWS Lake Formation FindMatches
Alternatives Considered:
A (Amazon Made pattern matching): Amazon Made is not a service in AWS, and pattern matching typically refers to regular expressions, which are not suitable for deduplication without a common identifier.
B (AWS Glue PySpark Filter class): PySpark's Filter class can help refine datasets, but it does not offer the ML-based matching capabilities required to find matches between records without unique identifiers.
C (Partition tables on a unique identifier): Partitioning requires a unique identifier, which the question states is unavailable.
References:
AWS Glue Documentation on Lake Formation FindMatches
FindMatches in AWS Lake Formation
NEW QUESTION # 47
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day.
The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: C
Explanation:
AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can create data quality checks to ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger an Amazon EventBridge event that sends a message to an SNS topic.
* AWS Glue Workflows:
* AWS Glue workflows allow users to automate and monitor complex ETL processes. You can include data quality actions to check for null values, data types, and other consistency checks.
* In the event of incomplete data, an EventBridge event can be generated to notify via SNS.
NEW QUESTION # 48
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross- account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal.
You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. References:
* Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams
NEW QUESTION # 49
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