Amazon DAS-C01 Online Practice Questions and Exam Preparation

Amazon DAS-C01 Online Questions & Answers

• Question 151:

  A large ecommerce company uses Amazon DynamoDB with provisioned read capacity and auto scaled write capacity to store its product catalog. The company uses Apache HiveQL statements on an Amazon EMR cluster to query the DynamoDB table. After the company announced a sale on all of its products, wait times for each query have increased. The data analyst has determined that the longer wait times are being caused by throttling when querying the table.

  Which solution will solve this issue?

  A. Increase the size of the EMR nodes that are provisioned.
  B. Increase the number of EMR nodes that are in the cluster.
  C. Increase the DynamoDB table's provisioned write throughput.
  D. Increase the DynamoDB table's provisioned read throughput.
  C. Increase the DynamoDB table's provisioned write throughput.

  ---

  Explanation/Reference:

• Question 152:

  An online retailer is rebuilding its inventory management system and inventory reordering system to automatically reorder products by using Amazon Kinesis Data Streams. The inventory management system uses the Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Kinesis Client Library (KCL) to consume data from the stream. The stream has been configured to scale as needed. Just before production deployment, the retailer discovers that the inventory reordering system is receiving duplicated data.

  Which factors could be causing the duplicated data? (Choose two.)

  A. The producer has a network-related timeout.
  B. The stream's value for the IteratorAgeMilliseconds metric is too high.
  C. There was a change in the number of shards, record processors, or both.
  D. The AggregationEnabled configuration property was set to true.
  E. The max_records configuration property was set to a number that is too high.
  A. The producer has a network-related timeout.
  C. There was a change in the number of shards, record processors, or both.

  ---

  Explanation/Reference:

  Duplication can happen in two ways, either in producer side or consumer side, obviously.

  In proudcer side it happens due to network delays/timeouts, specifically producer is waiting for an successful acknowledgement, yet it is lost due to network failure, and producer sends data again until it receives acknowledgement.

  In consumer side it happens due to record processor restart. This can happen due to 4 reasons. A worker terminates unexpectedly, Worker instances are added or removed, Shards are merged or split, application is deployed.

  In any of the situations the best way to prevent duplicates is to have a unique identifier in data.

• Question 153:

  A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.

  The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.

  Which configuration will provide the MOST cost-effective solution that meets these requirements?

  A. Load the data into an Amazon Redshift cluster by using the COPY command. Configure 50 author users and 1,000 reader users. Use QuickSight Enterprise edition. Configure an Amazon Redshift data source with a direct query option.
  B. Use QuickSight Standard edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source with a direct query option.
  C. Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source and import the data into SPICE. Automatically refresh every 24 hours.
  D. Use QuickSight Enterprise edition. Configure 1 administrator and 1,000 reader users. Configure an S3 data source and import the data into SPICE. Automatically refresh every 24 hours.
  C. Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source and import the data into SPICE. Automatically refresh every 24 hours.

  ---

  Explanation/Reference:

  When you create or edit a dataset, you choose to use either SPICE or a direct query, unless the dataset contains uploaded files. Importing (also called ingesting) your data into SPICE can save time and money:

  Your analytical queries process faster.

  You don't need to wait for a direct query to process.

  Data stored in SPICE can be reused multiple times without incurring additional costs. If you use a data source that charges per query, you're charged for querying the data when you first create the dataset and later when you refresh the

  dataset.

  Quotas for SPICE are as follows:

  2,047 Unicode characters for each field

  127 Unicode characters for each column name

  2,000 columns for each file

  1,000 files for each manifest

  For Standard edition, 25 million (25,000,000) rows or 25 GB for each dataset

  For Enterprise edition, 250 million (250,000,000) rows or 500 GB for each dataset

  https://docs.aws.amazon.com/quicksight/latest/user/spice.html

  https://docs.aws.amazon.com/quicksight/latest/user/data-source-limits.html

• Question 154:

  A team of data scientists plans to analyze market trend data for their company's new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical reprocessing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.

  Which solution meets these requirements?

  A. Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
  B. Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
  C. Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
  D. Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
  B. Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.

  ---

  Explanation/Reference:

  Multiple applications can consume from a single Kinesis Stream Kinesis Analytics for sql like queries for analysis Kinesis firhose can directly transfer the data into S3 from the same data stream

• Question 155:

  A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their

  data. The departments have some databases and tables that share common names.

  The marketing department needs to securely access some tables from the finance department.

  Which two steps are required for this process? (Choose two.)

  A. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.
  B. The finance department creates cross-account IAM permissions to the table for the marketing department role.
  C. The marketing department creates an IAM role that has permissions to the Lake Formation tables.
  A. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.
  C. The marketing department creates an IAM role that has permissions to the Lake Formation tables.

  ---

  Explanation/Reference:

  https://docs.aws.amazon.com/lake-formation/latest/dg/cross-account-permissions.html https://docs.aws.amazon.com/lake-formation/latest/dg/regranting-locations.html

• Question 156:

  A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.

  After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically.

  What should the data analyst do to resolve this?

  A. Increase the number of threads that process the stream records.
  B. Increase the provisioned read capacity units assigned to the stream's Amazon DynamoDB table.
  C. Increase the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
  D. Decrease the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
  C. Increase the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.

  ---

  Explanation/Reference:

  C is the right answer For each Amazon Kinesis Data Streams application, KCL uses a unique lease table (stored in a Amazon DynamoDB table) to keep track of the shards in a KDS data stream that are being leased and processed by the workers of the KCL consumer application.

  For each Amazon Kinesis Data Streams application, KCL uses a unique lease table (stored in a Amazon DynamoDB table) to keep track of the shards in a KDS data stream that are being leased and processed by the workers of the KCL consumer application.

  For each Amazon Kinesis Data Streams application, KCL uses a unique lease table (stored in a Amazon DynamoDB table) to keep track of the shards in a KDS data stream that are being leased and processed by the workers of the KCL consumer application.

• Question 157:

  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. The data 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?

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

  ---

  Explanation/Reference:

  Correct answer is B as the AWS Glue job can be updated to group files to create larger files which can help improve the processing time without any additional steps or changes.

  https://docs.aws.amazon.com/glue/latest/dg/grouping-input-files.html

  Options A and D are wrong as using a staging space or EMR would add additional steps to the processing.

  Option C is wrong as this only performs the loading of data, not processing before the load.

• Question 158:

  A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis Data Streams to collect the data streams from smart meters. The consumer

  application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.

  The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.

  Which solution meets these requirements?

  A. Use enhanced fan-out in Kinesis Data Streams.
  B. Increase the number of shards for the Kinesis data stream.
  C. Reduce the propagation delay by overriding the KCL default settings.
  D. Develop consumers by using Amazon Kinesis Data Firehose.
  C. Reduce the propagation delay by overriding the KCL default settings.

  ---

  Explanation/Reference:

  Correct answer is C as the default propagation delay for the KCL consumer is 1 second and reducing the delay can help reduce the latency.

  Propagation delay is defined as the end-to-end latency from the moment a record is written to the stream until it is read by a consumer application. This delay varies depending upon a number of factors, but it is primarily affected by the polling

  interval of consumer applications.

  Option A is wrong as enhanced fan-out in Kinesis Data Streams enables consumers to receive records from a stream with a throughput of up to 2 MB of data per second per shard. It doesn't reduce the latency for KCL.

  Option B is wrong as increasing shards doesn't reduce the latency for KCL.

  Option D is wrong as Kinesis Data Firehose calls Kinesis Data Streams GetRecords() once every second for each Kinesis shard.

• Question 159:

  A company with a video streaming website wants to analyze user behavior to make recommendations to users in real time Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3 The company wants a solution that can use standard SQL quenes The solution must also provide a way to look up pre- calculated reference data while making recommendations.

  Which solution meets these requirements?

  A. Use an AWS Glue Python shell job to process incoming data from Kinesis Data Streams Use the Boto3 library to write data to Amazon Redshift
  B. Use AWS Glue streaming and Scale to process incoming data from Kinesis Data Streams Use the AWS Glue connector to write data to Amazon Redshift
  C. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use a data stream to write results to Amazon Redshift
  D. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift Pass Your Certification With
  D. Use Amazon Kinesis Data Analytics to create an in-application table based upon the reference data Process incoming data from Kinesis Data Streams Use an Amazon Kinesis Data Firehose delivery stream to write results to Amazon Redshift Pass Your Certification With

  ---

  Explanation/Reference:

  Reference: https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/

• Question 160:

  An online gaming company wants to read customer data from Amazon Kinesis Data Streams and deliver the data to an Amazon S3 data lake for analytics. The data contains customer_id as one of the attributes. The data consumers need the data to be partitioned by customer_id in the S3 data lake.

  Which solution will meet this requirement with the LEAST effort?

  A. Create an Amazon Kinesis Data Firehose delivery stream. Use dynamic partitioning to partition the data by customer_id before delivering the data to Amazon S3.
  B. Create an AWS Glue streaming job Use the built-in map transform to partition the data by customer_id before delivering the data to Amazon S3.
  C. Create an AWS Lambda function. Use Lambda layers to partition the data by customer_id before delivering the data to Amazon S3.
  D. Create an Amazon EMR cluster. Run an Apache Spark job to automatically partition the data by customer_id before delivering the data to Amazon S3.
  A. Create an Amazon Kinesis Data Firehose delivery stream. Use dynamic partitioning to partition the data by customer_id before delivering the data to Amazon S3.