Amazon Data-Engineer-Associate Testantworten - Data-Engineer-Associate Fragen Beantworten

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q155-Q160):

155. Frage
A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor's data into the vendor's Amazon S3 bucket once each week.
Which solution will meet this requirement?

A. Create an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor's S3 bucket on a schedule.
B. Create an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor's S3 bucket on a schedule.
C. Configure Amazon Redshift Spectrum to use the vendor's S3 bucket as destination. Enable dataquerying in both directions.
D. Use the Amazon Redshift data sharing feature. Set the vendor's S3 bucket as the destination. Configure the source to be as a custom SQL query that selects the required data.

Antwort: A

Begründung:
TheRedshift UNLOADcommand is specifically designed to export query results toAmazon S3, andAWS Gluecan orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:
"Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.

156. Frage
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)

A. Select * from Employee where Region ID='North America';
B. Select * from Employee where Department ID=20 and Region ID='North America';
C. Select * from Employee where Region ID='North America' and Role ID=50;
D. Select * from Employee where Region ID='North America' and Department ID=20;
E. Select " from Employee where Role ID=50;

Antwort: B,D

Begründung:
In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.
Option B: "Select * from Employee where Region ID='North America' and Department ID=20;" This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.
Option C: "Select * from Employee where Department ID=20 and Region ID='North America';" This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.
Options A, D, and E are less optimal because they do not utilize the sort key as effectively:
Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.
Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.
Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.
Reference:
Amazon Redshift Documentation - Sorting Data
AWS Certified Data Analytics Study Guide
AWS Certification - Data Engineer Associate Exam Guide

157. Frage
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

A. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
D. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.

Antwort: C

Begründung:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using Amazon Timestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
* A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard.
Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream.
These steps can increase the latency and complexity of the solution.
* B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
* D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time.
Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
References:
* 1: Amazon Managed Streaming for Apache Flink
* 2: Amazon Timestream
* 3: Amazon QuickSight
* : Analyze data in Amazon Timestream using Grafana
* : Amazon Kinesis Data Firehose
* : Amazon Aurora
* : AWS Glue Bookmarks
* : AWS Glue Job and Crawler Scheduling

158. Frage
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A. Parallel state
B. Choice state
C. Map state
D. Wait state

Antwort: C

Begründung:
Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.
Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.
Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.
Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10 AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3

159. Frage
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?

A. Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Host the stream-processing application for each internal consumer on Amazon EC2 instances.Configure auto scaling for the EC2 instances.
B. Configure the mobile app to call the PutRecordBatch API operation to send data to Amazon Data Firehose. Submit an AWS Support case to turn on dedicated throughput for the company's AWS account. Allow each internal consumer to access the stream.
C. Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature with a stream for each internal consumer.
D. Configure the mobile app to use the Amazon Kinesis Producer Library (KPL) to send data to Amazon Data Firehose. Use the enhanced fan-out feature with a stream for each internal consumer.

Antwort: C

Begründung:
* Problem Analysis:
* Input Requirements: Gaming app generates approximately20 KB data records, which must be ingested and made available tothree internal consumerswithdedicated throughput.
* Key Requirements:
* High throughput for ingestion from each device.
* Dedicated processing bandwidth for each consumer.
* Key Considerations:
* Amazon Kinesis Data Streamssupports high-throughput ingestion withPutRecords APIfor batch writes.
* TheEnhanced Fan-Outfeature providesdedicated throughputto each consumer, avoiding bandwidth contention.
* This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.
* Solution Analysis:
* Option A: Kinesis Data Streams + Enhanced Fan-Out
* PutRecords API is designed for batch writes, improving ingestion performance.
* Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.
* Option B: Data Firehose + Dedicated Throughput Request
* Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.
* Option C: Data Firehose + Enhanced Fan-Out
* Firehose does not support enhanced fan-out. This option is invalid.
* Option D: Kinesis Data Streams + EC2 Instances
* Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.
* Final Recommendation:
* UseKinesis Data Streams with Enhanced Fan-Outfor high-throughput ingestion and dedicated consumer bandwidth.
:
Kinesis Data Streams Enhanced Fan-Out
PutRecords API for Batch Writes

160. Frage
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Amazon Data-Engineer-Associate Testantworten - Data-Engineer-Associate Fragen Beantworten

Data-Engineer-Associate Fragen Beantworten & Data-Engineer-Associate Lernhilfe

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q155-Q160):

Data-Engineer-Associate Studienmaterialien: AWS Certified Data Engineer - Associate (DEA-C01) - Data-Engineer-Associate Torrent Prüfung & Data-Engineer-Associate wirkliche Prüfung

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