DAS-C01 Exam: Absolutely Free Online Practice

Question 1
- 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?
  A : 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.
  
  B : Use Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a Kinesis Agent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.
  
  C : 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.
  
  D : 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.
  
  Answer: B
Question 2
- A company has a mobile app that has millions of users. The company wants to enhance the mobile app by including interactive data visualizations that show user trends. The data for visualization is stored in a large data lake with 50 million rows. Data that is used in the visualization should be no more than two hours old. Which solution will meet these requirements with the LEAST operational overhead?
  
  A :
  Run an hourly batch process that renders user-specific data visualizations as static images that are stored in Amazon S3.
  
  B :
  Precompute aggregated data hourly. Store the data in Amazon DynamoDB. Render the data by using the D3.js JavaScript library.
  
  C :
  Embed an Amazon QuickSight Enterprise edition dashboard into the mobile app by using the QuickSight Embedding SDK. Refresh data in SPICE hourly.
  
  D :
  Run Amazon Athena queries behind an Amazon API Gateway API. Render the data by using the D3.js JavaScript library.
  
  Answer: A
Question 3
- A data analyst is using Amazon QuickSight for data visualization across multiple datasets generated by applications. Each application stores files within a separate Amazon S3 bucket. AWS Glue Data Catalog is used as a central catalog across all application data in Amazon S3. A new application stores its data within a separate S3 bucket. After updating the catalog to include the new application data source, the data analyst created a new Amazon QuickSight data source from an Amazon Athena table, but the import into SPICE failed.
  How should the data analyst resolve the issue?
  A : Edit the permissions for the AWS Glue Data Catalog from within the Amazon QuickSight console.
  
  B : Edit the permissions for the new S3 bucket from within the Amazon QuickSight console.
  
  C : Edit the permissions for the AWS Glue Data Catalog from within the AWS Glue console.
  
  D : Edit the permissions for the new S3 bucket from within the S3 console.
  
  Answer: B
Question 4
- A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
  A data analyst notes the following:
  Approximately 90% of queries are submitted 1 hour after the market opens.
  Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
  Which solution would help address the performance issues?
  A : Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.
  
  B : Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.
  
  C : Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an
  automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.
  
  D : Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.
  
  Answer: D
Question 5
- A company has an application that ingests streaming dat
  a. 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?
  A : 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.
  
  B : 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 Firehouse
  
  C : Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute or 1 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.
  
  D : Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or 1 MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a 1-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.
  
  Answer: B

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