DAS-C01 Exam Questions Tutorials

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Question 16

A company’s marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:

The data size is approximately 32 TB uncompressed.
There is a low volume of single-row inserts each day.
There is a high volume of aggregation queries each day.
Multiple complex joins are performed.
The queries typically involve a small subset of the columns in a table.

Which storage service will provide the MOST performant solution?

Options:

Amazon Aurora MySQL

Amazon Redshift

Amazon Neptune

Amazon Elasticsearch

Question 17

A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.

Which approach would enable the desired outcome while keeping data persistence costs low?

Options:

Ingest the data stream with Amazon Kinesis Data Streams. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.

Ingest the data stream with Amazon Kinesis Data Streams. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status codes. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehose.

Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of I minute or I MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.

Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or I MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a I-minute window using the RCF function and summarize the count of status codes. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Question 18

A network administrator needs to create a dashboard to visualize continuous network patterns over time in a company's AWS account. Currently, the company has VPC Flow Logs enabled and is publishing this data to Amazon CloudWatch Logs. To troubleshoot networking issues quickly, the dashboard needs to display the new data in near-real time.

Which solution meets these requirements?

Options:

Create a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes the data to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

Create an export task from CloudWatch Logs to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

Create a CloudWatch Logs subscription that uses an AWS Lambda function to stream the CloudWatch Logs data directly into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

Create a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes to an Amazon Kinesis data stream to deliver the data into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

Question 19

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. Thedata engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.

More testing facilities were recently added, and the time to process files is increasing.

What will MOST efficiently decrease the data processing time?

Options:

Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.

Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.

Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.

Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.