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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
    :Jul 14, 2026

Amazon DAS-C01 Online Questions & Answers

  • Question 1:

    A real estate company maintains data about all properties listed in a market. The company receives data about new property listings from vendors who upload the data daily as compressed files into Amazon S3. The company's leadership team wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3. The data analytics team must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The team also must provide the ability to perform one-time queries and analytical reporting in a scalable manner.

    Which solution meets these requirements MOST cost-effectively?

    A. Use Amazon EMR for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Apache Hive for one-time queries and analytical reporting. Bulk ingest the data in Amazon OpenSearch Service (Amazon Elasticsearch Service). Use OpenSearch Dashboards (Kibana) on Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboard.
    B. Use Amazon EMR for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
    C. Use AWS Glue for processing incoming data. Use AWS Step Functions for workflow orchestration. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards (Kibana) on Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboard.
    D. Use AWS Glue for processing incoming data. Use AWS Lambda and S3 Event Notifications for workflow orchestration. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

  • Question 2:

    A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.

    1.

    A trips fact table for information on completed rides.

    2.

    A drivers dimension table for driver profiles.

    3.

    A customers fact table holding customer profile information.

    The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.

    What table design provides optimal query performance?

    A. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
    B. Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
    C. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
    D. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

  • Question 3:

    A company is providing analytics services to its sales and marketing departments. The departments can access the data only through their business intelligence (BI) tools, which run queries on Amazon Redshift using an Amazon Redshift internal user to connect. Each department is assigned a user in the Amazon Redshift database with the permissions needed for that department. The marketing data analysts must be granted direct access to the advertising table, which is stored in Apache Parquet format in the marketing S3 bucket of the company data lake. The company data lake is managed by AWS Lake Formation. Finally, access must be limited to the three promotion columns in the table.

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

    A. Grant permissions in Amazon Redshift to allow the marketing Amazon Redshift user to access the three promotion columns of the advertising external table.
    B. Create an Amazon Redshift Spectrum IAM role with permissions for Lake Formation. Attach it to the Amazon Redshift cluster.
    C. Create an Amazon Redshift Spectrum IAM role with permissions for the marketing S3 bucket. Attach it to the Amazon Redshift cluster.
    D. Create an external schema in Amazon Redshift by using the Amazon Redshift Spectrum IAM role. Grant usage to the marketing Amazon Redshift user.
    E. Grant permissions in Lake Formation to allow the Amazon Redshift Spectrum role to access the three promotion columns of the advertising table.
    F. Grant permissions in Lake Formation to allow the marketing IAM group to access the three promotion columns of the advertising table.

  • Question 4:

    A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster. The validation process needs to receive the posts for a given user in the order they were received by the Kinesis data stream.

    During peak hours, the social media posts take more than an hour to appear in the Amazon OpenSearch Service (Amazon ES) cluster. A data analytics specialist must implement a solution that reduces this latency with the least possible operational overhead.

    Which solution meets these requirements?

    A. Migrate the validation process from Lambda to AWS Glue.
    B. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
    C. Increase the number of shards in the Kinesis data stream.
    D. Send the posts stream to Amazon Managed Streaming for Apache Kafka instead of the Kinesis data stream.

  • Question 5:

    A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.

    Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners' dashboards?

    A. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.
    B. Create and schedule an AWS Glue Spark job to run every 5 minutes. The job inserts reference data into Amazon Redshift.
    C. Send reference data to Amazon Kinesis Data Streams. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
    D. Send the reference data to an Amazon Kinesis Data Firehose delivery stream. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

  • Question 6:

    A company uses Amazon OpenSearch Service (Amazon Elasticsearch Service) to store and analyze its website clickstream data. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day's worth of data in an Amazon ES cluster.

    The company has very slow query performance on the Amazon ES index and occasionally sees errors from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10 nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon EBS storage attached and the cluster is configured with 1,000 shards. Occasionally, JVMMemoryPressure errors are found in the cluster logs.

    Which solution will improve the performance of Amazon ES?

    A. Increase the memory of the Amazon ES master nodes.
    B. Decrease the number of Amazon ES data nodes.
    C. Decrease the number of Amazon ES shards for the index.
    D. Increase the number of Amazon ES shards for the index.

  • Question 7:

    A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (PII) Only authorized users should be able to see plaintext PII data.

    What is the MOST operationally efficient solution that meets these requirements?

    A. Define a bucket policy for each S3 bucket of the data lake to allow access to users who have authorization to see PII data. Catalog the data by using AWS Glue. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role.
    B. Register the S3 locations with AWS Lake Formation. Create two IAM roles. Use Lake Formation data permissions to grant Select permissions to all of the columns for one role. Grant Select permissions to only columns that contain non-PII data for the other role.
    C. Register the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an ETL workflow that removes the PII columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register the new S3 locations with Lake Formation. Grant users the permissions to each data lake data based on whether the users are authorized to see PII data.
    D. Register the S3 locations with AWS Lake Formation. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role. For each downstream analytics service, use its native security functionality and the IAM roles to secure the PII data.

  • Question 8:

    A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala. Operational management should be limited.

    Which combination of components can meet these requirements? (Choose three.)

    A. AWS Glue Data Catalog for metadata management
    B. Amazon EMR with Apache Spark for ETL
    C. AWS Glue for Scala-based ETL
    D. Amazon EMR with Apache Hive for JDBC clients
    E. Amazon Athena for querying data in Amazon S3 using JDBC drivers
    F. Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore

  • Question 9:

    An online retail company is migrating its reporting system to AWS. The company's legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.

    A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.

    Which solution meets these requirements?

    A. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are updated. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
    B. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an Amazon EMR cluster with consistent view enabled. Run emrfs sync before each analytics step to ensure data changes are updated. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
    C. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw dataset. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
    D. Use an S3 Select query to ensure that the data is properly updated. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

  • Question 10:

    A company needs to implement a solution to restrict the launch of new Amazon EMR clusters in public subnets. With the exception of SSH and HTTPS connections, no employee should be able to launch a new EMR cluster in a public subnet unless inbound traffic from the internet is blocked.

    Which combination of steps should the company take to meet this requirement? (Choose two.)

    A. Turn on EMR block public access for an IAM user group. Add all the employees to the group.
    B. Turn on EMR block public access for the account.
    C. Add port 443 as an exception in the block public access configuration.
    D. Add port 22 as an exception in the block public access configuration.
    E. Create a private internal subnet. Require all the employees to specify this subnet when they launch clusters.

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