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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 121:

    A telecommunications company needs to send customer call data records from its on-premise database to AWS to generate near-real-time insights. The solution captures and loads continuously changing updates from the operational data stores that run in PostgreSQL databases. A data analyst has configured an AWS Data Migration Service (AWS DMS) ongoing replication task to read changes in near-real time from the PostgreSQL source database transaction logs for each table and send the data to an Amazon Redshift cluster for further processing.

    The data analytics team has reported latency issues during the change data capture (CDC) of the AWS DMS task. The team thinks that the PostgreSQL databases are causing the high latency.

    Which set of actions will confirm that the PostgreSQL databases are the source of high latency?

    A. Enable Amazon CloudWatch for the AWS DMS task and look for the CDCIncomingChanges metric to identify delays in capturing the changes from the source database.
    B. Verify that logical replication is configured for the source database using the postgresql.conf configuration file.
    C. Enable Amazon CloudWatch Logs for the AWS DMS endpoint of the source database and check for error messages.
    D. Enable Amazon CloudWatch for the AWS DMS task and look for the CDCLatencySource metric to identify delays in capturing the changes from the source database.

  • Question 122:

    A media content company has a streaming playback application. The company wants to collect and analyze the data to provide near-real-time feedback on playback issues. The company needs to consume this data and return results within 30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over time.

    Which solution will allow the company to collect data for processing while meeting these requirements?

    A. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event trigger an AWS Lambda function to process the data. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.
    B. Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
    C. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
    D. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.

  • Question 123:

    A company's data science team is designing a shared dataset repository on a Windows server. The data repository will store a large amount of training data that the data science team commonly uses in its machine learning models. The data scientists create a random number of new datasets each day.

    The company needs a solution that provides persistent, scalable file storage and high levels of throughput and IOPS. The solution also must be highly available and must integrate with Active Directory for access control.

    Which solution will meet these requirements with the LEAST development effort?

    A. Store datasets as files in an Amazon EMR cluster. Set the Active Directory domain for authentication.
    B. Store datasets as files in Amazon FSx for Windows File Server. Set the Active Directory domain for authentication.
    C. Store datasets as tables in a multi-node Amazon Redshift cluster. Set the Active Directory domain for authentication.
    D. Store datasets as global tables in Amazon DynamoDB. Build an application to integrate authentication with the Active Directory domain.

  • Question 124:

    A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly. Once a new month begins, no new writes are made to any of the indices from the previous months. The company has been expanding the storage on the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs. Most searches on the cluster are on the most recent 3 months of data, while the audit team requires infrequent access to older data to generate periodic reports. The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs

    Which of the following is the MOST operationally efficient solution to meet these requirements?

    A. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier. When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.
    B. Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3. When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.
    C. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage.
    D. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage. When the audit team requires the older data, migrate the indices in UltraWarm storage back to hot storage.

  • Question 125:

    An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.

    Which options will help meet these requirements in the MOST efficient way? (Choose two.)

    A. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon OpenSearch Service (Amazon Elasticsearch Service).
    B. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon OpenSearch Service (Amazon Elasticsearch Service) from Amazon S3.
    C. Use Amazon OpenSearch Service (Amazon Elasticsearch Service) deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.
    D. Use OpenSearch Dashboards (Kibana) to aggregate, filter, and visualize the data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service). Refresh content performance dashboards in near-real time.
    E. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

  • Question 126:

    A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket. The financial statements must not be modified or deleted after they are saved to the S3 bucket. Which solution will meet these requirements?

    A. Create the S3 bucket with S3 Object Lock in governance mode.
    B. Create the S3 bucket with MFA delete enabled.
    C. Create the S3 bucket with S3 Object Lock in compliance mode.
    D. Create S3 buckets in two AWS Regions. Use S3 Cross-Region Replication (CRR) between the buckets.

  • Question 127:

    A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data.

    The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.

    Which solutions could the company implement to improve query performance? (Choose two.)

    A. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
    B. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
    C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
    D. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
    E. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.

  • Question 128:

    A large digital advertising company has built business intelligence (BI) dashboards in Amazon QuickSight Enterprise edition to understand customer buying behavior. The dashboards use the Super-fast, Parallel, In-memory Calculation Engine (SPICE) as the in-memory engine to store the data. The company's Amazon S3 data lake provides the data for these dashboards, which are queried using Amazon Athena. The data files used by the dashboards consist of millions of records partitioned by year, month, and hour, and new data is continuously added. Every data file in the data lake has a timestamp column named CREATE_TS, which indicates when the data was added or updated.

    Until now, the dashboards have been scheduled to refresh every night through a full reload. A data analyst must recommend an approach so the dashboards can be refreshed every hour, and include incremental data from the last hour.

    How can the data analyst meet these requirements with the LEAST amount of operational effort?

    A. Create new data partitions every hour in Athena by using the CREATE_TS column and schedule the QuickSight dataset to refresh every hour.
    B. Use direct querying in QuickSight by using Athena to make refreshed data always available.
    C. Use the CREATE_TS column to look back for incremental data in the last hour and schedule the QuickSight dataset to incrementally refresh every hour.
    D. Create new datasets in QuickSight to do a full reload every hour and add the datasets to SPICE.

  • Question 129:

    A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.

    Which solution achieves these required access patterns to minimize costs and administrative tasks?

    A. Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
    B. Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
    C. Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross-account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.
    D. Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket. On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.

  • Question 130:

    A data analyst notices the following error message while loading data to an Amazon Redshift cluster:

    “The bucket you are attempting to access must be addressed using the specified endpoint.”

    What should the data analyst do to resolve this issue?

    A. Specify the correct AWS Region for the Amazon S3 bucket by using the REGION option with the COPY command.
    B. Change the Amazon S3 object's ACL to grant the S3 bucket owner full control of the object.
    C. Launch the Redshift cluster in a VPC.
    D. Configure the timeout settings according to the operating system used to connect to the Redshift cluster.

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