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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q161-Q166):
NEW QUESTION # 161
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?
Answer: C
Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes.
References:
* Working with concurrency scaling - Amazon Redshift
* Amazon Redshift Concurrency Scaling - Amazon Web Services
* Configuring concurrency scaling queues - Amazon Redshift
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)
NEW QUESTION # 162
A data engineer needs to run a data transformation job whenever a user adds a file to an Amazon S3 bucket.
The job will run for less than 1 minute. The job must send the output through an email message to the data engineer. The data engineer expects users to add one file every hour of the day.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: D
NEW QUESTION # 163
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.
Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns. S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.
Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.
Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage. Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
Reference:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
What Is AWS Lake Formation? - AWS Lake Formation
Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation Using Bucket Policies and User Policies - Amazon Simple Storage Service Apache Ranger Apache Pig What Is Amazon Redshift? - Amazon Redshift
NEW QUESTION # 164
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?
Answer: B
Explanation:
To ensure that Branch B is up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform a git rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch.
* git rebase master:
* This command moves the commits of Branch B to be based on top of the latest state of the master branch. It allows the developer to resolve any conflicts and create a clean history.
NEW QUESTION # 165
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A.
The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?
Answer: B
Explanation:
To migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region (Account_A) to an Amazon Redshift cluster in the eu-west-1 Region (Account_B), AWS DMS needs a replication instance located in the target region (in this case, eu-west-1) to facilitate the data transfer between regions.
Option A: Set up an AWS DMS replication instance in Account_B in eu-west-1.
Placing the DMS replication instance in the target account and region (Account_B in eu-west-1) is the most efficient solution. The replication instance can connect to the source RDS PostgreSQL in eu-east-1 and migrate the data to the Redshift cluster in eu-west-1. This setup ensures data is replicated across AWS accounts and regions.
Options B, C, and D place the replication instance in either the wrong account or region, which increases complexity without adding any benefit.
Reference:
AWS Database Migration Service (DMS) Documentation
Cross-Region and Cross-Account Replication
NEW QUESTION # 166
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