Exam Data-Engineer-Associate Lab Questions - Data-Engineer-Associate Pass Test Guide

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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Operations and Support	22%	- Automate operational tasks - Ensure reliability and scalability - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines 1. CloudWatch, X-Ray, logging and metrics
Data Store Management	26%	- Design and implement data storage solutions 1. S3, Redshift, DynamoDB, RDS, Lake Formation 2. Data lakes, data warehouses, databases - Optimize storage performance and cost - Manage data lifecycle and storage tiers
Data Ingestion and Transformation	34%	- Ingest data from various sources 1. Use services like Kinesis, DMS, Glue, S3 2. Batch and streaming data ingestion - Transform and enrich data 1. Orchestrate data pipelines 2. Use Spark, EMR, Step Functions 3. Apply data processing logic - Implement data quality and validation
Data Security and Governance	18%	- Encrypt data at rest and in transit - Protect sensitive data - Enforce compliance and data governance 1. Data lineage, audit logging, regulatory requirements - Implement access control and authentication 1. IAM, Lake Formation, resource policies

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q218-Q223):

NEW QUESTION # 218
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
B. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
C. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
D. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.

Answer: B

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions.
S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration.
Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics.
Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* S3 Select and Glacier Select - Amazon Simple Storage Service
* AWS Lambda - FAQs
* What Is AWS Glue DataBrew? - AWS Glue DataBrew
* Populating the AWS Glue Data Catalog - AWS Glue
* What is Amazon Athena? - Amazon Athena

NEW QUESTION # 219
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable information (PII) about customers within the data.
The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

A. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.
B. Manually review the data for custom PII categories.
C. Implement custom data quality rules in Data Brew. Apply the custom rules across datasets.
D. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.

Answer: C

Explanation:
The data engineer needs to detect custom categories of PII within the data lake using AWS Glue DataBrew.
While DataBrew provides standard data quality rules, the solution must support custom PII categories.
* Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.This option is the most efficient because DataBrew allows the creation of custom data quality rules that can be applied to detect specific data patterns, including custom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.
Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.
References:
* AWS Glue DataBrew Documentation

NEW QUESTION # 220
A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.
A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team's data warehouse. The operational Aurora database must not be affected.
Which solution will meet these requirements with the LEAST operational effort?

A. Use the Aurora zero-ETL integration with Amazon Redshift to replicate the Orders table. Create a materialized view in Amazon Redshift to join with the Campaigns table.
B. Use AWS Glue to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.
C. Use federated queries to query the Orders table directly from Aurora. Create a materialized view in Amazon Redshift to join with the Campaigns table.
D. Use AW5 Database Migration Service (AWS DMS) Serverless to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.

Answer: B

NEW QUESTION # 221
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

A. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
C. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
D. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.

Answer: A

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross- account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal.
You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. References:
* Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams

NEW QUESTION # 222
A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years.

A. Configure an S3 Lifecycle policy to enable S3 Object Lock on all objects that are older than 7 years.
B. Use Amazon Athena to query the S3 bucket for objects that are older than 7 years. Configure Athena to delete the results.
C. Configure an S3 Lifecycle policy to move objects that are older than 7 years to S3 Glacier Deep Archive.
D. Configure an S3 Lifecycle policy to permanently delete objects that are older than 7 years.

Answer: D

Explanation:
S3 Lifecycle policies automate data retention and deletion. By specifying an expiration rule for 7 years, objects older than that period are permanently deleted without manual intervention.
"To automatically delete aged data, configure an S3 Lifecycle rule with an expiration policy for objects older than the retention period."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

NEW QUESTION # 223
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