P.S. Free 2026 Amazon Data-Engineer-Associate dumps are available on Google Drive shared by PrepPDF: https://drive.google.com/open?id=1917Ya47oEiySokNWbxh88mcJAXkKZ8Rj
Our PrepPDF provides the latest and the most complete Data-Engineer-Associate exam questions and answers aimed at becoming the most reliable dumps provider in IT exam software. With the help of our PrepPDF, nearly all those who have purchased our dumps have successfully passed the difficult Data-Engineer-Associate Exam, which gives us great confidence to recommend our reliable products to you. We can assure you that we will fully refund the cost you purchased our dump, if you fail Data-Engineer-Associate exam with our dumps. So, just rest assured to prepare for your exam.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Topic 2: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 3: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Topic 4: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
>> Guaranteed Data-Engineer-Associate Questions Answers <<
Our Data-Engineer-Associate Exam Braindumps have a broad market in most countries we have due to the high quality of the Data-Engineer-Associate exam dumps. The feedback of the customers is quite good since the pass rate is high, it helps them a lot. Some customers even promote our product to their friends or even colleges after they pass it. We offer free update for one year, it will help you to change your practicing ways in accordance with the dynamics of the exam.
NEW QUESTION # 288
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?
Answer: D
Explanation:
The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead.
Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:
Query editor V2
Working with materialized views
Scheduling queries
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 289
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?
Answer: A
Explanation:
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
* Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)
* When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)
NEW QUESTION # 290
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option D is correct because Aurora zero-ETL integration with Amazon Redshift is the AWS-managed feature built specifically to make transactional Aurora data available in Amazon Redshift Serverless in near real time.
AWS documents that Aurora zero-ETL is a fully managed solution that makes transactional data available in the analytics destination after it is written to the Aurora cluster, eliminating the need to build and maintain complex ETL pipelines. AWS also states that Redshift zero-ETL supports a target data warehouse that can be a Redshift Serverless workgroup, which directly matches the question.
Option A and C add more operational components, such as DMS tasks, replication design, schema handling, and additional streaming infrastructure. Option B is also more operationally heavy because MSK Connect with Debezium requires Kafka-based CDC infrastructure and connector management. Since the requirement emphasizes serverless, near real-time analytics, and least operational overhead, the native zero-ETL integration is the best fit. This also aligns with the study guide's focus on choosing AWS services that minimize management effort while supporting ingestion and analytics workflows.
NEW QUESTION # 291
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: D
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 # 292
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations.
The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.
Which combination of steps will meet this requirement? (Select TWO.)
Answer: D,E
Explanation:
* Problem Analysis:
* The company needscross-account accessto allow QuickSight inBI-Accountto interact with anS3 bucket in Hub-Account.
* The bucket is encrypted with anAWS KMS key.
* Appropriate permissions must be set for bothS3 accessandKMS decryption.
* Key Considerations:
* QuickSight requiresIAM permissionsto access S3 data and decrypt files using the KMS key.
* Both S3 and KMS permissions need to be properly configured across accounts.
* Solution Analysis:
* Option A: Use Existing KMS Key for Encryption
* While the existing KMS key is used for encryption, it must also grant decryption permissions to QuickSight.
* Option B: Add S3 Bucket to QuickSight Role
* Granting S3 bucket access to the QuickSight service role is necessary for cross-account access.
* Option C: AWS RAM for Bucket Sharing
* AWS RAM is not required; bucket policies and IAM roles suffice for granting cross- account access.
* Option D: IAM Policy for KMS Access
* QuickSight's service role in BI-Account needs explicit permissions to use the KMS key for decryption.
* Option E: Add KMS Key as Resource for Role
* The KMS key must explicitly list the QuickSight role as an entity that can access it.
* Implementation Steps:
* S3 Bucket Policy in Hub-Account:Add a policy to the S3 bucket granting the QuickSight service role access:
json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::<Bucket-Name>/*"
}
]
}
* KMS Key Policy in Hub-Account:Add permissions for the QuickSight role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": [
"kms:Decrypt",
"kms:DescribeKey"
],
"Resource": "*"
}
]
}
* IAM Policy for QuickSight Role in BI-Account:Attach the following policy to the QuickSight service role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"kms:Decrypt"
],
"Resource": [
"arn:aws:s3:::<Bucket-Name>/*",
"arn:aws:kms:<region>:<Hub-Account-ID>:key/<KMS-Key-ID>"
]
}
]
}
:
Setting Up Cross-Account S3 Access
AWS KMS Key Policy Examples
Amazon QuickSight Cross-Account Access
NEW QUESTION # 293
......
Do you long to get the Data-Engineer-Associate certification to improve your life? Are you worried about how to choose the learning product that is suitable for you? If your answer is yes, we are willing to tell you that you are a lucky dog, because you meet us, it is very easy for us to help you solve your problem. The Data-Engineer-Associatelatest question from our company can help people get their Data-Engineer-Associate certification in a short time.
Advanced Data-Engineer-Associate Testing Engine: https://www.preppdf.com/Amazon/Data-Engineer-Associate-prepaway-exam-dumps.html
BTW, DOWNLOAD part of PrepPDF Data-Engineer-Associate dumps from Cloud Storage: https://drive.google.com/open?id=1917Ya47oEiySokNWbxh88mcJAXkKZ8Rj