Amazon DAS-C01 Exam Practice Test Instant Access

Question 1

A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project.

Which solution meets these requirements?

AIngest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.

BCreate custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.

CIngest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.

DTake a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

Answer : C

Question 2

A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL.

Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams.

Which solution meets these requirements?

AIncrease the query priority to HIGHEST for the data analysis queue.

BConfigure the data analysis queue to enable concurrency scaling.

CCreate a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.

DUse workload management query queue hopping to route the query to the next matching queue.

Answer : D

Question 3

A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.

Which elements must each query monitoring rule include?

AA unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours

BA queue name, a unique rule name, and a predicate-based stop condition

CA unique rule name, one to three predicates, and an action

DA workload name, a unique rule name, and a query runtime-based condition

Answer : C

Question 4

A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.

Which solution will meet these requirements?

AUse Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.

BUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.

CUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.

DUse Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

Answer : C

This solution meets the requirements because:

Amazon Kinesis Data Firehose is a fully managed service that can capture, transform, and load streaming data into AWS data stores, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB1. It can receive data from sensors and other sources and deliver it to a destination with near-real time latency.

AWS Lambda is a serverless compute service that can run code in response to events and automatically manage the underlying compute resources2.It can be used to perform custom transformations on the data during capture by Kinesis Data Firehose3. It can aggregate the data according to the desired logic and output format.

Amazon DynamoDB is a fully managed NoSQL database service that supports key-value and document data models4. It can store nested JSON data as document attributes and provide single-digit millisecond latency for queries. It can be used as a persistent data store for the aggregated sensor data.

Question 5

A company collects data from parking garages. Analysts have requested the ability to run reports in near real time about the number of vehicles in each garage.

The company wants to build an ingestion pipeline that loads the data into an Amazon Redshift cluster. The solution must alert operations personnel when the number of vehicles in a particular garage exceeds a specific threshold. The alerting query will use garage threshold values as a static reference. The threshold values are stored in

Amazon S3.

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

AUse an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

BUse an Amazon Kinesis data stream to collect the data. Use an Amazon Kinesis Data Firehose delivery stream to deliver the data to Amazon Redshift. Create another Kinesis data stream to temporarily store the threshold values from Amazon S3. Send the delivery stream and the second data stream to Amazon Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an C. Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

CUse an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Automatically initiate an AWS Lambda function that queries the data in Amazon Redshift. Configure the Lambda function to compare the number of vehicles in a particular garage to the corresponding threshold value from Amazon S3. Configure the Lambda function to also publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

DUse an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Use Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value that is stored in a table as an in-application stream. Configure an AWS Lambda function as an output for the application to publish an Amazon Simple Queue Service (Amazon SQS) notification if the number of vehicles exceeds the threshold.

Answer : A

This solution meets the requirements because:

It uses Amazon Kinesis Data Firehose to collect and deliver data to Amazon Redshift in near real time, without requiring any coding or server management1.

It uses Amazon Kinesis Data Analytics to process and analyze streaming data using SQL queries or Apache Flink applications2.It can also create a reference data source that allows joining streaming data with static data stored in Amazon S33. This way, it can compare the number of vehicles in each garage with the corresponding threshold value from the reference data source.

It uses AWS Lambda to create a serverless function that can be triggered by Kinesis Data Analytics as an output destination4.The Lambda function can then publish an Amazon SNS notification to alert operations personnel when the number of vehicles exceeds the threshold5.

Question 6

A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.

What should the data analyst do to ensure the appropriate data security is in place?

APlace the data sources for Australia and the US into separate SPICE capacity pools.

BSet up an Amazon Redshift VPC security group for Australia and the US.

CDeploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

DDeploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

Answer : D

Question 7

An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the dat

a. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.

Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

AAdd a randomized string to the beginning of the keys in S3 to get more throughput across partitions.

BUse an S3 bucket in the same account as Athena.

CCompress the objects to reduce the data transfer I/O.

DUse an S3 bucket in the same Region as Athena.

EPreprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.

FPreprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

Answer : C, D, F

https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

Amazon AWS Certified Data Analytics - Specialty DAS-C01 Exam Practice Test