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AWS Certified Data Analytics AWS Certified Data Analytics - Specialty

AWS Certified Data Analytics - Specialty

Last Update Jul 26, 2024
Total Questions : 207

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Questions 4

A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.

How should a data analytics specialist design the solution for data ingestion?

Options:

Use Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.

Use Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a KinesisAgent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.

Use Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.

Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Discussion 0

Questions 5

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.

Which solution meets these requirements?

Options:

Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Use Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a custom application for the dashboard by using D3.js.

Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluster. Visualize the data by using a Kibana dashboard.

Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Discussion 0

Questions 6

A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.

Which solution meets these requirements?

Options:

Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.

Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.

Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the output to DynamoDB by using the default output from Kinesis Data Firehose.

Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.

Discussion 0

Questions 7

A large marketing company needs to store all of its streaming logs and create near-real-time dashboards. The dashboards will be used to help the company make critical business decisions and must be highly available.

Which solution meets these requirements?

Options:

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Create the dashboards by using Amazon QuickSight.

Deploy an Amazon Redshift cluster with at least three nodes in a VPC that spans two Availability Zones. Store the streaming logs and use the Redshift cluster as a source to create the dashboards by using Amazon QuickSight.

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Every time a new log is added in the bucket, invoke an AWS Lambda function to update the dashboards in Amazon QuickSight.

Store the streaming logs in Amazon OpenSearch Service deployed across three Availability Zones and with three dedicated master nodes. Create the dashboards by using OpenSearch Dashboards.

Discussion 0

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