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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q61-Q66):

NEW QUESTION # 61
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.)

A. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
B. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
C. Preprocess the .csvdata to JSON format by fetchingonly the document keys that the query requires.
D. Use an S3 bucket that is in the same account that uses Athena to query the data.
E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

Answer: A,E

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 # 62
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?

A. Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.
B. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.
C. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.
D. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.

Answer: B

Explanation:
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data. The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D) would not work, as the data on the instance store volume would be lost if the instance is terminated. References:
* Amazon EC2 Instance Store
* Amazon EBS Volumes
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2

NEW QUESTION # 63
A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics.
Which solution will give the company the ability to use Spark to access Athena?

A. Athena workgroup
B. Athena data source
C. Athena query settings
D. Athena query editor

Answer: B

Explanation:
Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.
The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. Reference:
Using Apache Spark in Amazon Athena
Athena JDBC Driver
Spark SQL
Athena query settings
[Athena workgroups]
[Athena query editor]

NEW QUESTION # 64
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 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.
B. 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.
C. 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.
D. 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.

Answer: C

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. Reference:
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 # 65
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
B. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.
C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

Answer: A

Explanation:
This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures.
References:
Amazon Managed Service for Apache Flink
Apache Flink
Window Aggregations in Flink

NEW QUESTION # 66
......

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Pass Guaranteed Quiz 2025 Unparalleled Data-Engineer-Associate: AWS Certified Data Engineer - Associate (DEA-C01) Dumps Discount

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q61-Q66):

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