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Amazon DAS-C01 Dumps PDF
AWS Certified Data Analytics - Specialty- 157 Questions & Answers
- Update Date : June 04, 2026
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Question 1
A business intelligence (Bl) engineer must create a dashboard to visualize how oftencertain keywords are used in relation to others in social media posts about a public figure.The Bl engineer extracts the keywords from the posts and loads them into an AmazonRedshift table. The table displays the keywords and the count correspondingto each keyword.The Bl engineer needs to display the top keywords with more emphasis on the mostfrequently used keywords.Which visual type in Amazon QuickSight meets these requirements?
A. Bar chartsB. Word clouds
C. Circle packing
D. Heat maps
Question 2
A company uses an Amazon Redshift provisioned cluster for data analysis. The data is notencrypted at rest. A data analytics specialist must implement a solution to encrypt the dataat rest.Which solution will meet this requirement with the LEAST operational overhead?
A. Use the ALTER TABLE command with the ENCODE option to update existing columnsof the Redshift tables to use LZO encoding.B. Export data from the existing Redshift cluster to Amazon S3 by using the UNLOADcommand with the ENCRYPTED option. Create a new Redshift cluster with encryptionconfigured. Load data into the new cluster by using the COPY command.
C. Create a manual snapshot of the existing Redshift cluster. Restore the snapshot into anew Redshift cluster with encryption configured.
D. Modify the existing Redshift cluster to use AWS Key Management Service (AWS KMS)encryption. Wait for the cluster to finish resizing.
Question 3
A company's data science team is designing a shared dataset repository on a Windowsserver. The data repository will store a large amount of training data that the datascience team commonly uses in its machine learning models. The data scientists create arandom number of new datasets each day.The company needs a solution that provides persistent, scalable file storage and highlevels of throughput and IOPS. The solution also must be highly available and mustintegrate with Active Directory for access control.Which solution will meet these requirements with the LEAST development effort?
A. Store datasets as files in an Amazon EMR cluster. Set the Active Directory domain forauthentication.B. Store datasets as files in Amazon FSx for Windows File Server. Set the Active Directorydomain for authentication.
C. Store datasets as tables in a multi-node Amazon Redshift cluster. Set the ActiveDirectory domain for authentication.
D. Store datasets as global tables in Amazon DynamoDB. Build an application to integrateauthentication with the Active Directory domain.
Question 4
A company is creating a data lake by using AWS Lake Formation. The data that will bestored in the data lake contains sensitive customer information and must be encrypted atrest using an AWS Key Management Service (AWS KMS) customer managed key to meetregulatory requirements.How can the company store the data in the data lake to meet these requirements?
A. Store the data in an encrypted Amazon Elastic Block Store (Amazon EBS) volume.Register the Amazon EBS volume with Lake Formation.B. Store the data in an Amazon S3 bucket by using server-side encryption with AWS KMS(SSE-KMS). Register the S3 location with Lake Formation.
C. Encrypt the data on the client side and store the encrypted data in an Amazon S3bucket. Register the S3 location with Lake Formation.
D. Store the data in an Amazon S3 Glacier Flexible Retrieval vault bucket. Register the S3Glacier Flexible Retrieval vault with Lake Formation.
Question 5
A financial company uses Amazon Athena to query data from an Amazon S3 data lake.Files are stored in the S3 data lake in Apache ORC format. Data analysts recentlyintroduced nested fields in the data lake ORC files, and noticed that queries are takinglonger to run in Athena. A data analysts discovered that more data than what is required isbeing scanned for the queries.What is the MOST operationally efficient solution to improve query performance?
A. Flatten nested data and create separate files for each nested dataset.B. Use the Athena query engine V2 and push the query filter to the source ORC file.
C. Use Apache Parquet format instead of ORC format.
D. Recreate the data partition strategy and further narrow down the data filter criteria.
Reviews
Practice questions covered security strategies for encrypting data at rest and in transit.
Practice material covered Kinesis scaling and shard management.
Practice sets clarified DynamoDB integration with analytics pipelines.
Practice material clarified security best practices with Lake Formation permissions.
The exam included integration scenarios with QuickSight dashboards.
Practice material explained designing scalable pipelines with AWS Glue workflows.
Practice sets covered data catalog design and management with Glue.
Practice sets explained using Glue Elastic Views for materialized aggregations.
Practice questions covered Glue crawlers and schema inference.
Data Lake formation and governance questions were tested directly.
Practice questions covered integration of analytics with S3 object versioning.
Practice questions helped me understand Redshift distribution styles and sort keys.
Practice material explained designing batch vs streaming architectures.
Practice material covered real-time log analysis using Kinesis Firehose.
What career benefits can the DAS-C01 certification bring for a data professional?
Practice sets clarified multi-region analytics strategies with S3 replication.
I faced questions on monitoring ETL pipelines with CloudWatch.
Practice sets helped me prepare for integrating data lakes with Lake Formation.
The exam tested partitioning and bucketing strategies in Athena and S3.
The exam tested securing analytics pipelines with KMS keys.
I had questions about Athena query cost control strategies.
The exam tested federated queries using Redshift Spectrum and Athena.
The exam included questions on optimizing queries in Redshift using materialized views.
The exam tested incremental data processing strategies in Glue jobs.
Practice material covered Aurora integration with analytics pipelines.
The exam included ML model integration with Redshift ML.
Practice sets explained row vs columnar storage for different workloads.
Practice material clarified schema evolution handling in Glue and Athena.
The exam tested hybrid analytics pipelines across on-premises and AWS.
The exam included IAM policies for controlling access to analytics resources.
The exam tested compression techniques for improving Redshift performance.
Practice sets clarified real-time dashboarding with Kinesis and QuickSight.
The exam included Glue job retries and error handling strategies.
The DAS-C01 exam tested streaming data solutions with Amazon Kinesis Data Streams and Firehose.
The exam tested Redshift concurrency scaling and workload management.
Practice sets explained anomaly detection with Kinesis and Lambda.
Practice questions helped me understand optimizing large-scale joins in Redshift.
Practice questions covered Redshift Spectrum external table optimization.
Practice sets clarified real-time analytics with Amazon Kinesis Data Analytics.
The exam included Glue ETL job design and optimization scenarios.