Most Recent Amazon DAS-C01 Exam Dumps

Prepare for the Amazon AWS Certified Data Analytics - Specialty exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Amazon DAS-C01 exam and achieve success.

The questions for DAS-C01 were last updated on Mar 28, 2025.

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Question No. 1

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.

Which solution meets these requirements?

AUnload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.

BExport the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.

CCreate an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.

DExport the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

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Correct Answer: C

https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html

Question No. 2

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?

ASend 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.

BSend 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.

CSend 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.

DSend 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.

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Correct Answer: D

https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/

Question No. 3

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?

AConnect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.

BConnect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.

CConnect 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.

DConnect 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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Correct Answer: C

Question No. 4

A data engineer is using AWS Glue ETL jobs to process data at frequent intervals The processed data is then copied into Amazon S3 The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job

Which solution will meet these requirements MOST cost-effectively?

AUse the AWS Glue Data Catalog to manage the data catalog Define an AWS Glue workflow for the ETL process Define a trigger within the workflow that can start the crawler when an ETL job run is complete

BUse the AWS Glue Data Catalog to manage the data catalog Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.

CUse an Apache Hive metastore to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

DUse the AWS Glue Data Catalog to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

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Correct Answer: A

Question No. 5

A financial services firm is processing a stream of real-time data from an application by using Apache Kafka and Kafka MirrorMaker. These tools run on premises and stream data to Amazon Managed Streaming for Apache Kafka (Amazon MSK) in the us-east-1 Region. An Apache Flink consumer running on Amazon EMR enriches the data in real time and transfers the output files to an Amazon S3 bucket. The company wants to ensure that the streaming application is highly available across AWS Regions with an RTO of less than 2 minutes.

Which solution meets these requirements?

ALaunch another Amazon MSK and Apache Flink cluster in the us-west-1 Region that is the same size as the original
cluster in the us-east-1 Region. Simultaneously publish and process the data in both Regions. In the event of a disaster that impacts one of the Regions, switch to the other Region.

BSet up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

CAdd an AWS Lambda function in the us-east-1 Region to read from Amazon MSK and write to a global Amazon

DDynamoDB table in on-demand capacity mode. Export the data from DynamoDB to Amazon S3 in the us-west-1 Region. In the event of a disaster that impacts the us-east-1 Region, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

ESet up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region. Store 7 days of data in on-premises Kafka clusters and recover the data missed during the recovery time from the on-premises cluster.

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Correct Answer: A

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