BTW, DOWNLOAD part of ExamBoosts Data-Engineer-Associate dumps from Cloud Storage: https://drive.google.com/open?id=1R-IZ5I33TM5Q5Oo3h9oUzOPCkCfeVAfO
We provide 3 versions for the client to choose and free update. Different version boosts different advantage and please read the introduction of each version carefully before your purchase. The language of our Data-Engineer-Associate study materials are easy to be understood and we compile the Data-Engineer-Associate Exam Torrent according to the latest development situation in the theory and the practice. You only need little time to prepare for our exam. So it is worthy for you to buy our Data-Engineer-Associate questions torrent.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Protect sensitive data - Encrypt data at rest and in transit - Enforce compliance and data governance
|
| Topic 2: Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery - Automate operational tasks |
| Topic 3: Data Ingestion and Transformation | 34% | - Implement data quality and validation - Ingest data from various sources
|
| Topic 4: Data Store Management | 26% | - Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions
|
>> Data-Engineer-Associate Latest Demo <<
The AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) PDF dumps format can be accessed from any smart device such as laptops, tablets, and smartphones. ExamBoosts regularly updates the Amazon Data-Engineer-Associate PDF Questions to reflect the latest Amazon Data-Engineer-Associate exam content. All test questions in the AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam PDF format are real and latest.
NEW QUESTION # 214
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud.
It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
* B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
* C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
* D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
:
1: AWS Data Exchange User Guide
2: AWS Data Exchange FAQs
3: AWS Glue Developer Guide
4: AWS CodeCommit User Guide
5: Amazon Kinesis Data Streams Developer Guide
6: Amazon Elastic Container Registry User Guide
7: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
NEW QUESTION # 215
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)
Answer: A,C
Explanation:
The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.
Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.
Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can beused for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.
Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. References:
Amazon S3 - Cloud Object Storage
EMR File System (EMRFS)
AWS Graviton2 Processor-Powered Amazon EC2 Instances
[Plan and Configure EC2 Instances]
[Amazon EC2 Spot Instances]
[Best Practices for Amazon EMR]
NEW QUESTION # 216
A data engineer needs to query data from multiple sources to generate an annual report. The analytics team uses Amazon Redshift for analysis. The data engineer needs to integrate Amazon Redshift data with 10 years of historical data from Amazon RDS for PostgreSQL and RDS for MySQL. All the databases are in the same VPC. The data engineer needs a solution that provides seamless data integration with Amazon Redshift.
Which solution will meet these requirements in the MOST cost-effective way?
Answer: B
Explanation:
Option A is the most cost-effective because it enables seamless integration by allowing analysts to access and join external relational data directly from within Amazon Redshift for reporting, without building and operating a separate ingestion pipeline for a one-time (annual) workload. The study material frames Amazon Redshift as the centralized analytics store for complex queries and reporting, making it the natural place to perform the final transformations and analysis.
The alternatives introduce extra operational steps and recurring costs. Option B requires exporting large historical datasets to S3 and then loading them into Redshift, which increases data movement and operational complexity. Option C adds ETL job development, scheduling, retries, and monitoring overhead that is unnecessary if the primary goal is integrated querying for an annual report. Option D (DMS) is best when you need ongoing migration or continuous replication; it is typically more operationally heavy than needed for
"query across sources" use cases and still requires additional setup and maintenance.
Because all databases are already in the same VPC and the analytics platform is Redshift, federated querying provides the most direct, lowest-operations path to integrate and analyze the data where it already needs to be consumed.
NEW QUESTION # 217
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?
Answer: B
Explanation:
Problem Analysis:
The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.
Data updates must propagate quickly to OpenSearch for analytics or search purposes.
Key Considerations:
DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).
Integration with AWS Lambda allows seamless processing of these changes.
OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.
Solution Analysis:
Option A: Step Functions with Periodic Export
Not suitable for near-real-time updates; introduces significant latency.
Operationally complex to manage periodic exports and S3 data ingestion.
Option B: AWS Glue Job
AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.
Option C: DynamoDB Streams + Lambda
DynamoDB Streams capture changes in near real time.
Lambda can process these streams and use the OpenSearch API to update the index.
This approach provides low latency and seamless integration with minimal operational overhead.
Option D: Custom OpenSearch Plugin
Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.
Implementation Steps:
Enable DynamoDB Streams for the relevant DynamoDB tables.
Create a Lambda function to process stream records:
Parse insert, update, and delete events.
Use OpenSearch APIs to index or update documents based on the event type.
Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.
Monitor and log errors for debugging and operational health.
Reference:
Amazon DynamoDB Streams Documentation
AWS Lambda and DynamoDB Integration
Amazon OpenSearch Service APIs
NEW QUESTION # 218
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option A is correct because AWS Glue is a serverless ETL service built to run PySpark workloads at scale with minimal infrastructure management. AWS documentation states that you can install additional Python modules and libraries for use with AWS Glue ETL jobs, including by using the --additional-python-modules parameter and Amazon S3 paths for wheel artifacts or other supported package delivery methods. That directly addresses the requirement for custom internal libraries. Since the data is already in Amazon S3 and the result must be loaded into Amazon Redshift, Glue is a natural low-overhead fit for this daily transformation pipeline.
Option B and C require managing compute infrastructure or cluster lifecycle, which increases operational overhead. Option D is not the best fit because SageMaker Processing is designed primarily for ML-oriented data preparation, not as the standard AWS service for large-scale ETL into Redshift. The question explicitly asks for least operational overhead with scalable PySpark and custom libraries, and AWS Glue provides exactly that managed capability.
NEW QUESTION # 219
......
ExamBoosts is an experienced website with great reputation which offering Amazon dumps torrent and professional explanations. Our Data-Engineer-Associate test questions are created by our IT elites who pay great attention to the IT exam certification so we can ensure you the authority and reliability of our Data-Engineer-Associate Practice Test.
Data-Engineer-Associate Cert Guide: https://www.examboosts.com/Amazon/Data-Engineer-Associate-practice-exam-dumps.html
BTW, DOWNLOAD part of ExamBoosts Data-Engineer-Associate dumps from Cloud Storage: https://drive.google.com/open?id=1R-IZ5I33TM5Q5Oo3h9oUzOPCkCfeVAfO