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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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195
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1
Question
A hospital uses an electronic health records (EHR) system to collect two types of data
• Patient information, which includes a patient's name and address
• Diagnostic tests conducted and the results of these tests
Patient information is expected to change periodically Existing diagnostic test data never changes and only new records are added The hospital runs an Amazon Redshift cluster with four dc2.large nodes and wants to automate the ingestion of the patient information and diagnostic test data into respective Amazon Redshift tables
for analysis The EHR system exports data as CSV files to an Amazon S3 bucket on a daily basis Two
sets of CSV files are generated One set of files is for patient information with updates, deletes, and inserts The other set of files is for new diagnostic test data only
What is the MOST cost-effective solution to meet these requirements?
• Patient information, which includes a patient's name and address
• Diagnostic tests conducted and the results of these tests
Patient information is expected to change periodically Existing diagnostic test data never changes and only new records are added The hospital runs an Amazon Redshift cluster with four dc2.large nodes and wants to automate the ingestion of the patient information and diagnostic test data into respective Amazon Redshift tables
for analysis The EHR system exports data as CSV files to an Amazon S3 bucket on a daily basis Two
sets of CSV files are generated One set of files is for patient information with updates, deletes, and inserts The other set of files is for new diagnostic test data only
What is the MOST cost-effective solution to meet these requirements?
Options
A
Use AWS Database Migration Service (AWS DMS) to collect and process change data capture (CDC) records Use the COPY command to load patient information data into the staging tables. Use a stored procedure to handle create, update and delete operations for the patient information table
B
Use an AWS Glue crawler to catalog the data in Amazon S3 Use Amazon Redshift Spectrum to perform scheduled queries of the data in Amazon S3 and ingest the data into the patient information table and the diagnostic tests table.
C
Use an AWS Lambda function to run a COPY command that appends new diagnostic test data to the diagnostic tests table Run another COPY command to load the patient information data into the staging tables Use a stored procedure to handle create update, and delete operations for the patient information table
D
Use Amazon EMR with Apache Hudi. Run daily ETL jobs using Apache Spark and the Amazon Redshift JDBC driver
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2
Question
An education provider’s learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider’s LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with
optimal performance. System users will query the most recent 4 months of data 95% of the time
while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?
optimal performance. System users will query the most recent 4 months of data 95% of the time
while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?
Options
A
Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.
B
Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.
C
Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
D
Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.
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3
Question
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?
Which steps will satisfy the security requirements?
Options
A
For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon
S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the
service role for the EMR cluster EC2 instances to the trust polices for the base IAM roles. Create a
security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the
service role for the EMR cluster EC2 instances to the trust polices for the base IAM roles. Create a
security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
B
For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon
S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the
service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create
a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
S3. Create three additional IAM roles, each granting access to each team's specific bucket. Add the
service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create
a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
C
For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon
S3. Create three additional IAM roles, each granting access to each team’s specific bucket. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust policy. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
S3. Create three additional IAM roles, each granting access to each team’s specific bucket. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust policy. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
D
For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon
S3. Create three additional IAM roles, each granting access to each team’s specific bucket. Add the
service role for the EMR cluster EC2 instances to the trust polices for the additional IAM roles. Create
a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
S3. Create three additional IAM roles, each granting access to each team’s specific bucket. Add the
service role for the EMR cluster EC2 instances to the trust polices for the additional IAM roles. Create
a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
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4
Question
A company receives datasets from partners at various frequencies. The datasets include baseline data and incremental data. The company needs to merge and store all the datasets without reprocessing the data.
Which solution will meet these requirements with the LEAST development effort?
Which solution will meet these requirements with the LEAST development effort?
Options
A
Use an AWS Glue job with job bookmarks enabled to process the datasets. Store the data in Amazon S3.
B
Use an Apache Spark job in an Amazon EMR cluster to process the datasets. Store the data in EMR File System (EMRFS).
C
Use an AWS Glue job with a temporary table to process the datasets. Store the data in an Amazon RDS table.
D
Use an AWS Lambda function to process the datasets. Store the data in Amazon S3.
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5
Question
A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time.
Which solution meets these requirements?
Which solution meets these requirements?
Options
A
Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.
B
Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
C
Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
D
Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.
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