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Amazon DAS-C01 Online Practice Questions and Exam Preparation

DAS-C01 Exam Details

  • Exam Code
    :DAS-C01
  • Exam Name
    :AWS Certified Data Analytics - Specialty (DAS-C01)
  • Certification
    :Amazon Certifications
  • Vendor
    :Amazon
  • Total Questions
    :285 Q&As
  • Last Updated
    :May 26, 2026

Amazon DAS-C01 Online Questions & Answers

  • Question 141:

    An online advertising company wants to perform sentiment analysis of social media data to measure the success of online advertisements. The company wants to implement an end-to-end streaming solution to continuously ingest data from various social networks, clean and transform the streaming data in near-real time, and make the data available for analytics and visualization with Amazon QuickSight. The company wants a solution that is easy to implement and manage so it can design better analytics solutions instead of provisioning and maintaining infrastructure.

    Which solution meets these requirements with the LEAST amount of operational effort?

    A. Use Amazon Kinesis Data Firehose to ingest the data. Author an AWS Glue streaming ETL job to transform the ingested data. Load the transformed data into an Amazon Redshift table.
    B. Use Apache Kafka running on Amazon EC2 instances to ingest the data. Create an Amazon EMR Spark job to transform the ingested data. Use the COPY command to load the transformed data into an Amazon Redshift table.
    C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Create an Amazon EMR Spark job to transform the ingested data. Use the COPY command to load the transformed data into an Amazon Redshift table.
    D. Use Amazon Kinesis Data Streams to ingest the data. Author an AWS Glue streaming ETL job to transform the ingested data. Load the transformed data into an Amazon Redshift table.

  • Question 142:

    A retail company that is based in the United States has launched a global website. The website's historic transaction data is stored in an Amazon Redshift cluster in a VPC in the us-east-1 Region. The company's business intelligence (BI) team wants to enhance user experience by providing a dashboard to visualize trends.

    The BI team decides to use Amazon QuickSight to render the dashboards. During development, a team in Japan provisioned QuickSight in the ap-northeast-1 Region. However, the team cannot connect from QuickSight in ap-northeast-1 to the Amazon Redshift cluster in us-east-1.

    Which solution will resolve this issue MOST cost-effectively?

    A. In the Amazon Redshift console, configure Cross-Region snapshots. Set the destination Region as ap-northeast-1. Restore the Amazon Redshift cluster from the snapshot. Connect to QuickSight in ap-northeast-1.
    B. Create a VPC endpoint from the QuickSight VPC to the Amazon Redshift VPC.
    C. Create an Amazon Redshift endpoint connection string with Region information in the string. Use this connection string in QuickSight to connect to Amazon Redshift.
    D. Create a new security group for the Amazon Redshift cluster in us-east-1. Add an inbound rule that allows access from the appropriate IP address range for the QuickSight servers in ap-northeast-1.

  • Question 143:

    A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-realtime data.

    Which visualization solution will meet these requirements?

    A. Select Amazon OpenSearch Service (Amazon Elasticsearch Service) as the endpoint for Kinesis Data Firehose. Set up an OpenSearch Dashboards (Kibana) using the data in Amazon OpenSearch Service (Amazon ES) with the desired analyses and visualizations.
    B. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
    C. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
    D. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.

  • Question 144:

    A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.

    Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

    A. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Choose Amazon OpenSearch Service (Amazon Elasticsearch Service) as the end destination of the weblogs.
    B. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatch. Choose Amazon OpenSearch Service (Amazon Elasticsearch Service) as the end destination of the weblogs.
    C. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Configure Splunk as the end destination of the weblogs.
    D. Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatch. Configure Amazon DynamoDB as the end destination of the weblogs.

  • Question 145:

    A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster.

    A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the

    structure of the JSON file. The company requires a solution with the least possible implementation effort.

    Which combination of steps will create a solution that meets these requirements? (Choose three.)

    A. Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.
    B. Use an AWS Glue crawler to catalog the data.
    C. Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.
    D. Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.
    E. Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.
    F. Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

  • Question 146:

    A technology company has an application with millions of active users every day. The company queries daily usage data with Amazon Athena to understand how users interact with the application. The data includes the date and time, the location ID, and the services used. The company wants to use Athena to run queries to analyze the data with the lowest latency possible.

    Which solution meets these requirements?

    A. Store the data in Apache Avro format with the date and time as the partition, with the data sorted by the location ID.
    B. Store the data in Apache Parquet format with the date and time as the partition, with the data sorted by the location ID.
    C. Store the data in Apache ORC format with the location ID as the partition, with the data sorted by the date and time.
    D. Store the data in .csv format with the location ID as the partition, with the data sorted by the date and time.

  • Question 147:

    A company's marketing and finance departments are storing data in Amazon S3 in their respective AWS accounts managed by AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data in Amazon S3. The finance department needs to share some tables with the marketing department for reporting purposes.

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

    A. The finance department grants Lake Formation permissions for the shared tables to the marketing department's AWS account.
    B. The finance department creates a cross-account IAM role with permission to access the shared tables.
    C. Users from the marketing department account assume a cross-account IAM role in the finance department account that has permission to access the shared tables.
    D. The marketing department creates a resource link to access the shared tables from the finance department.
    E. The finance department creates and shares a resource link with the marketing department to access the shared tables.

  • Question 148:

    A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.

    Which solution will meet these requirements?

    A. Use Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.
    B. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.
    C. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.
    D. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

  • Question 149:

    An ecommerce company uses Amazon Aurora PostgreSQL to process and store live transactional data and uses Amazon Redshift for its data warehouse solution. A nightly ETL job has been implemented to update the Redshift cluster with new data from the PostgreSQL database. The business has grown rapidly and so has the size and cost of the Redshift cluster. The company's data analytics team needs to create a solution to archive historical data and only keep the most recent 12 months of data in Amazon Redshift to reduce costs. Data analysts should also be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data.

    Which combination of tasks will meet these requirements? (Choose three.)

    A. Configure the Amazon Redshift Federated Query feature to query live transactional data in the PostgreSQL database.
    B. Configure Amazon Redshift Spectrum to query live transactional data in the PostgreSQL database.
    C. Schedule a monthly job to copy data older than 12 months to Amazon S3 by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.
    D. Schedule a monthly job to copy data older than 12 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Redshift Spectrum to access historical data with S3 Glacier Flexible Retrieval.
    E. Create a late-binding view in Amazon Redshift that combines live, current, and historical data from different sources.
    F. Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

  • Question 150:

    A retail company's data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports.

    Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?

    A. Separate the data by product and use S3 bucket policies for authorization.
    B. Separate the data by product and use IAM policies for authorization.
    C. Create a manifest file with row-level security.
    D. Create dataset rules with row-level security.

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