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Helping Hand Questions for Data-Engineer-Associate

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Question 32

A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.

Which factors could cause to the permissions-related errors? (Choose two.)

Options:

A.

There is no connection between QuickSgqht and Athena.

B.

The Athena tables are not cataloged.

C.

QuickSiqht does not have access to the S3 bucket.

D.

QuickSight does not have access to decrypt S3 data.

E.

There is no 1AM role assigned to QuickSiqht.

Question 33

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?

Options:

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.

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.

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.

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.

Question 34

A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.

A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.

Which solution will meet this requirement?

Options:

A.

Design the application so it can remove duplicates during processing by embedding a unique ID in each record at the source.

B.

Update the checkpoint configuration of the Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) data collection application to avoid duplicate processing of events.

C.

Design the data source so events are not ingested into Kinesis Data Streams multiple times.

D.

Stop using Kinesis Data Streams. Use Amazon EMR instead. Use Apache Flink and Apache Spark Streaming in Amazon EMR.

Question 35

A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.

The company must ensure that the application performs consistently during peak usage times.

Which solution will meet these requirements in the MOST cost-effective way?

Options:

A.

Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.

B.

Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.

C.

Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.

D.

Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

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Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
Last Update: Nov 14, 2024
Questions: 130
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