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AWS-Certified-Data-Analytics-Specialty Certification Practice -

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    Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam Sample Questions (Q99-Q104):

    NEW QUESTION # 99
    A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
    Which visualization solution will meet these requirements?

    • A. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
    • B. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.
    • C. Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
    • D. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.

    Answer: C


    NEW QUESTION # 100
    A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage.
    Which solution meets these requirements with the least operational overhead?

    • A. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.
    • B. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
    • C. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
    • D. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.

    Answer: C


    NEW QUESTION # 101
    A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
    Which steps should a data analyst take to accomplish this task efficiently and securely?

    • A. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.
    • B. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.
    • C. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.
    • D. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key.
      Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.

    Answer: C


    NEW QUESTION # 102
    A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection.
    Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and Amazon Aurora MySQL.
    Which solution will provide the MOST up-to-date results?

    • A. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
    • B. Query all the datasets in place with Apache Presto running on Amazon EMR.
    • C. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.
    • D. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.

    Answer: A


    NEW QUESTION # 103
    A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications mobile apps and operational databases The company has migrated some of these sources to AWS and now wants to migrate the Flink application. The company must ensure that data that resides in databases within the VPC does not traverse the internet The application must be able to process all the data that comes from the company's AWS solution, on-premises resources and the public internet Which solution will meet these requirements with the LEAST operational overhead?

    • A. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink jar file Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the company's VPC to collect data that comes from applications and databases within the VPC Use Amazon Kinesis Data Streams to collect data that comes from the public internet Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams. Amazon MSK and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
    • B. Implement Flink on Amazon EC2 within the company's VPC Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet Configure Flink to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
    • C. Implement Flink on Amazon EC2 within the company's VPC Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the VPC to collect data that comes from applications and databases within the VPC Use Amazon Kinesis Data Streams to collect data that comes from the public internet Configure Flink to have sources from Kinesis Data Streams Amazon MSK and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
    • D. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink jar file Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect

    Answer: A


    NEW QUESTION # 104
    ......

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