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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines
- Ensure reliability and scalability |
| Topic 2: Data Store Management | 26% | - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Topic 3: Data Ingestion and Transformation | 34% | - Ingest data from various sources
- Transform and enrich data
|
| Topic 4: Data Security and Governance | 18% | - Protect sensitive data - Encrypt data at rest and in transit - Enforce compliance and data governance
|
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NEW QUESTION # 48
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)
Answer: A,C
NEW QUESTION # 49
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
The best solution for managing SSL/TLS certificates on EC2 instances with minimal operational overhead is to use AWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.
AWS Certificate Manager (ACM):
ACM manages SSL/TLS certificates for EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.
ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.
Reference:
Alternatives Considered:
A (Self-managed certificates): Managing certificates manually on EC2 instances increases operational overhead and lacks automatic renewal.
C (Secrets Manager automation): While Secrets Manager can store keys and certificates, it requires custom automation for rotation and does not handle SSL/TLS certificates directly.
D (ECS Service Connect): This is unrelated to SSL/TLS certificate management and would not address the operational need.
AWS Certificate Manager Documentation
NEW QUESTION # 50
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based on the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
Answer: A
Explanation:
For query workloads on S3 data that depend on date-based filters, partitioning by order date optimizes performance and cost because Athena reads only the relevant partitions.
Athena scales automatically and doesn't degrade with increasing data size when partitions are managed efficiently.
"Partitioning data in Amazon S3 based on query predicates such as order date improves Athena query performance and reduces scanned data volume."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This is the most cost-effective and scalable option for date-based queries.
NEW QUESTION # 51
A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?
Answer: D
Explanation:
Problem Analysis:
The pipeline processes compressed files in S3 and must support incremental data processing.
AWS Glue features must facilitate tracking progress to avoid reprocessing the same data.
Key Considerations:
Incremental data processing requires tracking which files or partitions have already been processed.
The solution must be automated and efficient for large-scale ETL jobs.
Solution Analysis:
Option A: Workflows
Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.
Option B: Triggers
Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.
Option C: Job Bookmarks
Job bookmarks track the state of the data that has been processed, enabling incremental processing.
Automatically skip files or partitions that were previously processed in Glue jobs.
Option D: Classifiers
Classifiers determine the schema of incoming data but do not handle incremental processing.
Final Recommendation:
Job bookmarks are specifically designed to enable incremental data processing in AWS Glue ETL pipelines.
Reference:
AWS Glue Job Bookmarks Documentation
AWS Glue ETL Features
NEW QUESTION # 52
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don't have to manage any infrastructure.
AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.
AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.
Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:
* AWS Glue Workflows
* AWS Step Functions
* AWS Glue Studio
* Amazon MWAA
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 53
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