1. Home
  2. Amazon
  3. DAS-C01

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 201:

    A company is running Apache Spark on an Amazon EMR cluster. The Spark job writes data to an Amazon S3 bucket and generates a large number of PUT requests. The number of objects has increased over time.

    After a recent increase in traffic, the Spark job started failing and returned an HTTP 503 Slow Down AmazonS3Exception error.

    Which combination of actions will resolve this error? (Choose two.)

    A. Increase the number of S3 key prefixes for the S3 bucket.
    B. Increase the EMR File System (EMRFS) retry limit.
    C. Disable dynamic partition pruning in the Spark configuration for the cluster.
    D. Increase the repartitioning number for the Spark job.
    E. Increase the executor memory size on Spark.

  • Question 202:

    A large marketing company needs to store all of its streaming logs and create near-real-time dashboards. The dashboards will be used to help the company make critical business decisions and must be highly available.

    Which solution meets these requirements?

    A. Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Create the dashboards by using Amazon QuickSight.
    B. Deploy an Amazon Redshift cluster with at least three nodes in a VPC that spans two Availability Zones. Store the streaming logs and use the Redshift cluster as a source to create the dashboards by using Amazon QuickSight.
    C. Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Every time a new log is added in the bucket, invoke an AWS Lambda function to update the dashboards in Amazon QuickSight.
    D. Store the streaming logs in Amazon OpenSearch Service deployed across three Availability Zones and with three dedicated master nodes. Create the dashboards by using OpenSearch Dashboards.

  • Question 203:

    A manufacturing company is storing data from its operational systems in Amazon S3. The company's business analysts need to perform one-time queries of the data in Amazon S3 with Amazon Athena. The company needs to access the Athena network from the on-premises network by using a JDBC connection. The company has created a VPC Security policies mandate that requests to AWS services cannot traverse the Internet.

    Which combination of steps should a data analytics specialist take to meet these requirements? (Choose two.)

    A. Establish an AWS Direct Connect connection between the on-premises network and the VPC.
    B. Configure the JDBC connection to connect to Athena through Amazon API Gateway.
    C. Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3.
    D. Configure the JDBC connection to use an interface VPC endpoint for Athena.
    E. Deploy Athena within a private subnet.

  • Question 204:

    A company has a process that writes two datasets in CSV format to an Amazon S3 bucket every 6 hours. The company needs to join the datasets, convert the data to Apache Parquet, and store the data within another bucket for users to query using Amazon Athena. The data also needs to be loaded to Amazon Redshift for advanced analytics. The company needs a solution that is resilient to the failure of any individual job component and can be restarted in case of an error.

    Which solution meets these requirements with the LEAST amount of operational overhead?

    A. Use AWS Step Functions to orchestrate an Amazon EMR cluster running Apache Spark. Use PySpark to generate data frames of the datasets in Amazon S3, transform the data, join the data, write the data back to Amazon S3, and load the data to Amazon Redshift.
    B. Create an AWS Glue job using Python Shell that generates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job at the desired frequency.
    C. Use AWS Step Functions to orchestrate the AWS Glue job. Create an AWS Glue job using Python Shell that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift.
    D. Create an AWS Glue job using PySpark that creates dynamic frames of the datasets in Amazon S3, transforms the data, joins the data, writes the data back to Amazon S3, and loads the data to Amazon Redshift. Use an AWS Glue workflow to orchestrate the AWS Glue job.

  • Question 205:

    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?

    A. 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 apnortheast-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. 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.

  • Question 206:

    A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third-party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.

    Which options can fulfill these requirements? (Choose two.)

    A. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.
    B. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.
    C. Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.
    D. Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with DynamoDB Streams to install the software.
    E. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance. Create an AMI and use that AMI to create the EMR cluster.

  • Question 207:

    An online retail company has an application that runs on Amazon EC2 instances launched in a VPC. The company wants to build a solution that allows the security team to collect VPC Flow Logs and analyze network traffic. Which solution MOST cost-effectively meets these requirements?

    A. Publish VPC Flow Logs to Amazon CloudWatch Logs and use Amazon Athena for analytics.
    B. Publish VPC Flow Logs to Amazon CloudWatch Logs and stream log data to an Amazon OpenSearch Service cluster for analytics.
    C. Publish VPC Flow Logs to Amazon S3 in text format and use Amazon Athena for analytics.
    D. Publish VPC Flow Logs to Amazon S3 in Apache Parquet format and use Amazon Athena for analytics.

  • Question 208:

    A telecommunications company stores its call records as JSON files in an Amazon S3 bucket. The company uses Amazon Athena to query the records and wants to improve query performance. The data is stored in data records that have up to 300 different columns. The most common query uses a subset of only 12 of the columns.

    Which solution will improve the query performance?

    A. Convert the data to Apache Parquet files by using native JSON Serializer/Deserializer (SerDe) libraries.
    B. Convert the data to Apache Parquet files by using Amazon EMR. Compress the files by using Snappy.
    C. Convert the data to Apache Parquet files by using Amazon EMR. Compress the files by using gzip.
    D. Convert the data to Apache Avro files by using Athena. Compress the files by using bzip2.

  • Question 209:

    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?

    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.

  • Question 210:

    A media company has a streaming playback application. The company needs to collect and analyze data to provide near-real-time feedback on playback issues within 30 seconds. The company requires a consumer application to identify playback issues, such as decreased quality during a specified time frame. The data will be streamed in JSON format. The schema can change over time.

    Which solution will meet these requirements?

    A. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event to invoke an AWS Lambda function to process and analyze the data.
    B. Send the data to Amazon Managed Streaming for Apache Kafka. Configure Amazon Kinesis Data Analytics for SQL Application as the consumer application to process and analyze the data.
    C. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to initiate an event for AWS Lambda to process and analyze the data.
    D. Send the data to Amazon Kinesis Data Streams. Configure an Amazon Kinesis Data Analytics for Apache Flink application as the consumer application to process and analyze the data.

Related Exams:

Tips on How to Prepare for the Exams

Nowadays, the certification exams become more and more important and required by more and more enterprises when applying for a job. But how to prepare for the exam effectively? How to prepare for the exam in a short time with less efforts? How to get a ideal result and how to find the most reliable resources? Here on Vcedump.com, you will find all the answers. Vcedump.com provide not only Amazon exam questions, answers and explanations but also complete assistance on your exam preparation and certification application. If you are confused on your DAS-C01 exam preparations and Amazon certification application, do not hesitate to visit our Vcedump.com to find your solutions here.