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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Topic 2: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Topic 3: Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Topic 4: Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
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NEW QUESTION # 81
A company uses AWS Key Management Service (AWS KMS) to encrypt an Amazon Redshift cluster. The company wants to configure a cross-Region snapshot of the Redshift cluster as part of disaster recovery (DR) strategy.
A data engineer needs to use the AWS CLI to create the cross-Region snapshot.
Which combination of steps will meet these requirements? (Select TWO.)
Answer: C,E
Explanation:
To perform cross-Region snapshot copying of an encrypted Redshift cluster, AWS documentation and the exam study guide clearly outline two essential steps:
* You must create a snapshot copy grant in thedestination Region.This allows Amazon Redshift to encrypt the snapshots using the specified AWS KMS key.
* You must enable snapshot copying in thesource Regionand specify the name of the snapshot copy grant that was created in thedestination Region.
From the study guide:
"To enable cross-region copy of encrypted snapshots, you must create a snapshot copy grant in the destination Region and enable snapshot copying in the source Region by specifying the snapshot copy grant name."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf OptionE(Multi-AZ deployment) is not applicable to Amazon Redshift, which does not support Multi-AZ configurations like Amazon RDS.
NEW QUESTION # 82
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here's why:
* Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.
* Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.
* Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.
* Alternatives Considered:
* Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard- IA directly would be more cost-effective since the pattern of access is well-known (frequent for
30 days, infrequent thereafter).
* Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.
* Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.
:
Amazon S3 Lifecycle Policies
S3 Storage Classes
Cost Management and Data Optimization Using Lifecycle Policies
AWS Data Engineering Documentation
NEW QUESTION # 83
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 # 84
A company has an Amazon Redshift data warehouse that users access by using a variety of IAM roles. More than 100 users access the data warehouse every day.
The company wants to control user access to the objects based on each user's job role, permissions, andhow sensitive the data is.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon Redshift supportsRole-Based Access Control (RBAC)to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.
"RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.
NEW QUESTION # 85
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: B,C
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 # 86
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