Most Recent Amazon DAS-C01 Exam Questions & Answers

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 Nov 14, 2024.

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Question No. 1

A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.

Which solution meets these requirements?

AUse Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

BLoad all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.

CUse an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.

DCreate a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

Show Answer

Correct Answer: D

https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html 'You can use a manifest to ensure that the COPY command loads all of the required files, and only the required files, for a data load'

Question No. 2

A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.

Which Amazon Redshift feature meets the requirements for this task?

AConcurrency scaling

BShort query acceleration (SQA)

CWorkload management (WLM)

DMaterialized views

Show Answer

Correct Answer: D

Materialized views:

Question No. 3

A company wants to ingest clickstream data from its website into an Amazon S3 bucket. The streaming data is in JSON format. The data in the S3 bucket must be partitioned by product_id.

Which solution will meet these requirements MOST cost-effectively?

ACreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Enable dynamic partitioning. Specify the data field of productjd as one partitioning key.

BCreate an AWS Glue streaming job to partition the data by productjd before delivering the data to the S3 bucket. Create an Amazon Kinesis Data Firehose delivery stream. Specify the AWS Glue job as the destination of the delivery stream.

CCreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an AWS Glue ETL job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

DCreate an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an Amazon EMR cluster that includes a job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

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Correct Answer: A

Question No. 4

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.

Show Answer

Correct Answer: D

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

Question No. 5

A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (Pll). Only authorized users should be able to see

plaintext PII data.

What is the MOST operationally efficient solution that meets these requirements?

ADefine a bucket policy for each S3 bucket of the data lake to allow access to users who have authorization to see PII data. Catalog the data by using AWS Glue. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role.

BRegister the S3 locations with AWS Lake Formation. Create two IAM roles. Use Lake Formation data permissions to grant Select permissions to all of the columns for one role. Grant Select permissions to only columns that contain non-PII data for the other role.

CRegister the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an E TL workflow that removes the Pll columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register the new S3 locations with Lake Formation. Grant users the permissions to each data lake data based on whether the users are authorized to see PII data.

DRegister the S3 locations with AWS Lake Formation. Create two IAM roles. Attach a permissions policy with access to Pll columns to one role. Attach a policy without these permissions to the other role. For each downstream analytics service, use its native security functionality and the IAM roles to secure the Pll data.

Show Answer

Correct Answer: B

This solution meets the requirements because:

AWS Lake Formation is a fully managed service that allows you to build, secure, and manage data lakes on AWS1.You can use Lake Formation to register your S3 locations as data sources and catalog your data using AWS Glue1.

AWS Lake Formation provides fine-grained data permissions that enable you to control access to your data at the column or row level1.You can use Lake Formation to create two IAM roles and grant them different Select permissions based on the PII status of the columns1.

AWS Lake Formation integrates with various analytics services from AWS, such as Amazon Athena, Amazon Redshift, Amazon EMR, and Amazon QuickSight1.You can use these services to query and visualize your data in S3 using the IAM roles and permissions defined by Lake Formation1.

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