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NEW QUESTION: 1
Which fundamental qualitative characteristics make accounting information useful for decision-making?
A. Assets and liability
B. Income and expenses
C. Relevance and faithful representation
D. Cost and materiality
Answer: C

NEW QUESTION: 2
Note: This question is part of series of questions that present the same scenario. Each question in the series contain a unique solution. Determine whether the solution meets the stated goals.
You develop data engineering solutions for a company.
A project requires the deployment of resources to Microsoft Azure for batch data processing on Azure HDInsight. Batch processing will run daily and must:
Scale to minimize costs
Be monitored for cluster performance
You need to recommend a tool that will monitor clusters and provide information to suggest how to scale.
Solution: Download Azure HDInsight cluster logs by using Azure PowerShell.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Instead monitor clusters by using Azure Log Analytics and HDInsight cluster management solutions.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-oms-log-analytics-tutorial

NEW QUESTION: 3
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, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have Azure IoT Edge devices that generate streaming data.
On the devices, you need to detect anomalies in the data by using Azure Machine Learning models. Once an anomaly is detected, the devices must add information about the anomaly to the Azure IoT Hub stream.
Solution: You expose a Machine Learning model as an Azure web service.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies:
temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection