What's more, part of that PassLeader Data-Engineer-Associate dumps now are free: https://drive.google.com/open?id=1KHlWSbZtl4e5cqitifKSVedhboQUj9Ma
Our Data-Engineer-Associate exam torrent offers you free demo to try before buying. You will get your downing link and password after the payment, and you can download Data-Engineer-Associate exam dumps right now. If you have any questions, you can directly contact us through online live chat or you can notify us through email, we will give you reply as soon as we can. In addition, we provide you free update for one year after purchasing the Data-Engineer-Associate Exam Dumps.
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
>> Data-Engineer-Associate Premium Files <<
If you have any problems installing and using Data-Engineer-Associate study engine, you can contact our staff immediately. You know, we have so many users. If you do not immediately receive a link from us, you can send us an email to urge us. We hope you can use our Data-Engineer-Associate Exam simulating as soon as possible! Our system is very smooth and you basically have no trouble. We hope you enjoy using our Data-Engineer-Associate study engine.
NEW QUESTION # 210
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)
Answer: D,E
Explanation:
Using an S3 bucket that is in the same AWS Region where the company runs Athena queries can improve query performance by reducing data transfer latency and costs. Preprocessing the .csv data to Apache Parquet format can also improve query performance by enabling columnar storage, compression, and partitioning, which can reduce the amount of data scanned and fetched by the query. These solutions can optimize the storage solution for the POC test without requiring much effort or changes to the existing data pipeline. The other solutions are not optimal or relevant for this requirement. Adding a randomized string to the beginning of the keys in Amazon S3 can improve the throughput across partitions, but it can also make the data harder to query and manage. Using an S3 bucket that is in the same account that uses Athena to query the data does not have any significant impact on query performance, as long as the proper permissions are granted.
Preprocessing the .csv data to JSON format does not offer any benefits over the .csv format, as both are row- based and verbose formats that require more data scanning and fetching than columnar formats like Parquet.
References:
* Best Practices When Using Athena with AWS Glue
* Optimizing Amazon S3 Performance
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 211
A company uses AWS Glue ETL pipelines to process data. The company uses Amazon Athena to analyze data in an Amazon S3 bucket.
To better understand shipping timelines, the company decides to collect and store shipping dates and delivery dates in addition to order data. The company adds a data quality check to ensure that the shipping date is later than the order date and that the delivery date is later than the shipping date. Orders that fail the quality check must be stored in a second Amazon S3 bucket.
Which solution will meet these requirements in the MOST cost-effective way?
Answer: C
Explanation:
AWS Glue Data Quality is designed specifically to validate data as part of ETL pipelines with minimal cost and operational overhead. By creating a custom data quality rule, the data engineer can enforce business logic such as ensuring that the shipping date is later than the order date and that the delivery date is later than the shipping date.
Glue Data Quality rules run natively within AWS Glue ETL jobs, eliminating the need for additional services, duplicate queries, or post-processing steps. The service supports automatically identifying failed records and routing them to a separate Amazon S3 bucket, which directly satisfies the requirement to isolate invalid orders.
Using AWS Glue DataBrew introduces additional profiling and transformation steps that are unnecessary for a simple rule-based validation. Athena-based validation would require scanning data repeatedly, increasing query costs and adding complexity. AWS Glue crawlers only discover schema metadata and cannot enforce data quality rules.
From a cost and architecture perspective, Glue Data Quality integrates directly into existing Glue pipelines, avoids additional compute usage, and provides centralized monitoring of data quality metrics. Therefore, Option C is the most cost-effective and exam-aligned solution.
NEW QUESTION # 212
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
Answer: B
Explanation:
In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.
IAM Roles for Service Accounts (IRSA):
With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.
This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.
Reference:
Alternatives Considered:
A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.
C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.
D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long-term credentials manually, which is less secure than using IRSA.
IAM Best Practices for Amazon EKS
Secure Access to DynamoDB from EKS
NEW QUESTION # 213
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. Reference:
Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore) Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog) AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)
NEW QUESTION # 214
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?
Answer: D
Explanation:
Problem Analysis:
The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.
The goal is to implement quality checks with minimal implementation effort.
Key Considerations:
AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.
DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.
Solution Analysis:
Option A: SQL Transform
SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.
Option B: Evaluate Data Quality Transform + DQDL
AWS Glue's built-in Evaluate Data Quality transform is designed for this use case.
Allows defining thresholds and rules in DQDL with minimal coding effort.
Option C: Custom Transform with PyDeequ
PyDeequ is a powerful library but adds unnecessary complexity compared to Glue's native features.
Option D: Custom Transform with Great Expectations
Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.
Final Recommendation:
Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.
Reference:
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation
NEW QUESTION # 215
......
For candidates who are going to buy Data-Engineer-Associate exam torrent online, you may pay much attention to the privacy protection. We respect the private information of you, if you choose us for your Data-Engineer-Associate exam materials, your personal information will be protected well. Once the order finishes, your personal information such as your name and email address will be concealed. In addition, we have a professional team to research the professional knowledge for Data-Engineer-Associate Exam Materials, and you can get the latest information timely. Free update for one year is available, and the update version for Data-Engineer-Associate training material will be sent to your email automatically.
Data-Engineer-Associate Examcollection Questions Answers: https://www.passleader.top/Amazon/Data-Engineer-Associate-exam-braindumps.html
What's more, part of that PassLeader Data-Engineer-Associate dumps now are free: https://drive.google.com/open?id=1KHlWSbZtl4e5cqitifKSVedhboQUj9Ma