Free AWS Certified Data Analytics - Specialty (DAS-C01) Exam Questions and Answers

Last updated: September 12, 2026

Exam code

DAS-C01

Provider

Amazon

Questions in our bank

1000+

Free to read

First 10, with answers

Official page

Official Exam website

Our test mode duration & pass mark

130 mins · 70%

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1. Question #1

   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 comman
   • B. Configure 50 author users and 1,000 reader user
   • C. Use QuickSight Enterprise editio
   • D. Configure an Amazon Redshift data source with a direct query option.
   Reveal Answer

2. Question #2

   A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format. Which approach can provide the visuals that senior leadership requested with the least amount of effort?

   • A. Use Amazon QuickSight with Amazon Athena as the data sourc
   • B. Use heat maps as the visual type.
   • C. Use Amazon QuickSight with Amazon S3 as the data sourc
   • D. Use heat maps as the visual type.
   Reveal Answer

3. Question #3

   A manufacturing company uses Amazon Connect to manage its contact center and Salesforce to manage its customer relationship management (CRM) data. The data engineering team must build a pipeline to ingest data from the contact center and CRM system into a data lake that is built on Amazon S3. What is the MOST efficient way to collect data in the data lake with the LEAST operational overhead?

   • A. Use Amazon Kinesis Data Streams to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.
   • B. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon Kinesis Data Streams to ingest Salesforce data.
   • C. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.
   • D. Use Amazon AppFlow to ingest Amazon Connect data and Amazon Kinesis Data Firehose to ingest Salesforce data.
   Reveal Answer

4. Question #4

   A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data. What should the data analyst do to ensure the appropriate data security is in place?

   • A. Place the data sources for Australia and the US into separate SPICE capacity pools.
   • B. Set up an Amazon Redshift VPC security group for Australia and the US.
   • C. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.
   • D. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.
   Reveal Answer

5. Question #5

   A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately. The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer. Which solution would achieve this goal?

   • A. Have the app call the PutRecords API to send data to Amazon Kinesis Data Stream
   • B. Use the enhanced fan-out feature while consuming the data.
   • C. Have the app call the PutRecordBatch API to send data to Amazon Kinesis Data Firehos
   • D. Submit a support case to enable dedicated throughput on the account.
   Reveal Answer

6. Question #6

   A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort. Which solution meets these requirements?

   • A. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
   • B. Use Apache Spark Streaming on Amazon EMR to read the data in near-real tim
   • C. Develop a custom application for the dashboard by using D3.js.
   • D. Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluste
   Reveal Answer

7. Question #7

   A company has an application that ingests streaming data. 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 Stream
   • B. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF mode
   • C. Persist the source and results as a time series to Amazon DynamoDB.
   • D. Ingest the data stream with Amazon Kinesis Data Stream
   Reveal Answer

8. Question #8

   A company is sending historical datasets to Amazon S3 for storage. A data engineer at the company wants to make these datasets available for analysis using Amazon Athena. The engineer also wants to encrypt the Athena query results in an S3 results location by using AWS solutions for encryption. The requirements for encrypting the query results are as follows: Use custom keys for encryption of the primary dataset query results. Use generic encryption for all other query results. Provide an audit trail for the primary dataset queries that shows when the keys were used and by whom. Which solution meets these requirements?

   • A. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primary datase
   • B. Use SSE-S3 for the other datasets.
   • C. Use server-side encryption with customer-provided encryption keys (SSE-C) for the primary dataset.Use server-side encryption with S3 managed encryption
   • D. Use server-side encryption with AWS KMS managed customer master keys (SSE-KMS CMKs) for the primary datase
   Reveal Answer

9. Question #9

   An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync Which solution will simplify permissions management with minimal development effort?

   • A. Set AWS Identity and Access Management (1AM) permissions tor AWS Glue
   • B. Use AWS Lake Formation permissions
   • C. Manage AWS Glue and S3 permissions by using bucket policies
   • D. Use Amazon Cognito user pools.
   Reveal Answer

10. Question #10

    A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs. What is an explanation for this behavior and what is the solution?

    • A. There are multiple shards in a stream and order needs to be maintained in the shar
    • B. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
    • C. The hash key generation process for the records is not working correctl
    • D. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
    Reveal Answer