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NEW QUESTION: 1
You need to ensure that phone-based poling data can be analyzed in the PollingData database.
How should you configure Azure Data Factory?
A. Use a schedule trigger
B. Use manual execution
C. Use an event-based trigger
D. Use a tumbling schedule trigger
Answer: A
Explanation:
When creating a schedule trigger, you specify a schedule (start date, recurrence, end date etc.) for the trigger, and associate with a Data Factory pipeline.
Scenario:
All data migration processes must use Azure Data Factory
All data migrations must run automatically during non-business hours
References:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-schedule-trigger

NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You deploy a Virtual Machine Scale Set (VMSS) named CorpWebVMSS to Azure by using
Azure PowerShell and set the instance count to 1. The VMSS includes a storage account, load balancer, public IP address. and six Standard_A1 Windows virtual machines (VMs) that run Internet Information Services (IIS). All components are deployed to a resource group named CorpWebRG.
You must increase the instance count to support the increased load on IIS.
You need to manually scale out the number of VMs in the scale set to 5.
Solution: You deploy the following JSON template by using Azure PowerShell:

Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
References:
https://docs.microsoft.com/en-us/azure/virtual-machine-scale-sets/virtual-machine-scale- sets-autoscale-overview

NEW QUESTION: 3
A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors.
Which solution enables the company to meet these requirements?
A. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.
B. Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side.
Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten the JSON file before sending it to an Amazon S3 bucket. Load the S3 data into an Amazon Redshift cluster.
C. Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side.
Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
D. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon Elasticsearch Service cluster.
Answer: C

NEW QUESTION: 4
Identify the scenarios when the Goal Comment Notification is triggered. There are 3 correct answers to this question.
A. When a goal with a threaded feedback/comment is cascaded to another employee
B. When a separate user (ex. Matrix manager) provides a feedback on the employee's goal plan, both the employee and the employee's manager receives an email notification
C. When a goal with a threaded feedback/comment is deleted from the goal plan
D. When an employee provides a feedback response in the goal plan, the direct manager receives an email notification
E. When an employee receives feedback on their goal plan from their direct line manager, the employee receives an email notification
Answer: B,D,E