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AWS Certified Data Analytics – Specialty DAS-C01 Exam (DAS-C01) - AWS Exam Questions
Last updated on July 22, 2026
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1
Question
A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s dat
Options
A
An EVEN distribution style for the product table and an KEY distribution style for the transactions
B
A KEY distribution style for both tables
C
An ALL distribution style for the product table and an EVEN distribution style for the transactions table table
D
An EVEN distribution style for both tables
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2
Question
A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends. The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.
Which solution will solve this issue and meet the requirements?
Which solution will solve this issue and meet the requirements?
Options
A
Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.
B
Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.
C
Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.
D
In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in ap-northeast-1.
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3
Question
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company’s requirements?
Which architectural pattern meets company’s requirements?
Options
A
Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.
Configure the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
Configure the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
B
Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view.
Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.
Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.
C
Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
Run two separate EMR clusters in two different Availability Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
Run two separate EMR clusters in two different Availability Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
D
Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
Create a primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
Create a primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
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4
Question
A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?
Which solution meets these requirements?
Options
A
Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
B
Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
C
Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the output to DynamoDB by using the default output from Kinesis Data Firehose.
D
Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.
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5
Question
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 Athen
a. 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?
wants to make these datasets available for analysis using Amazon Athen
a. 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?
Options
A
Use client-side encryption with AWS Key Management Service (AWS KMS) customer managed keys for the primary dataset. Use S3 client-side encryption with client-side keys for the other datasets.
B
Use server-side encryption with customer-provided encryption keys (SSE-C) for the primary
dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
C
Use server-side encryption with AWS KMS managed customer master keys (SSE-KMS CMKs) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.D
D
Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primary dataset. Use SSE-S3 for the other datasets.
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