- (Exam Topic 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, 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 deploy Azure Stream Analytics as an IoT Edge module. Does this meet the goal?
Correct Answer:
A
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
- (Exam Topic 2)
You have thousands of images that contain text.
You need to process the text from the images into a machine-readable character stream. Which Azure Cognitive Services service should you use?
Correct Answer:
C
With Computer Vision you can detect text in an image using optical character recognition (OCR) and extract the recognized words into a machine-readable character stream.
References:
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/ https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/image-moderation-api
- (Exam Topic 2)
You need to deploy cognitive search. You provision an Azure Search service. What should you do next?
Correct Answer:
D
You create a data source, a skillset, and an index. These three components become part of an indexer that pulls each piece together into a single multi-phased operation.
Note: At the start of the pipeline, you have unstructured text or non-text content (such as image and scanned document JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.
Indexers can "crack" source documents to extract text from source data. References:
https://docs.microsoft.com/en-us/azure/search/cognitive-search-tutorial-blob
- (Exam Topic 2)
You are designing a solution that will use the Azure Content Moderator service to moderate user-generated content.
You need to moderate custom predefined content without repeatedly scanning the collected content. Which API should you use?
Correct Answer:
A
The default global list of terms in Azure Content Moderator is sufficient for most content moderation needs. However, you might need to screen for terms that are specific to your organization. For example, you might
want to tag competitor names for further review.
Use the List Management API to create custom lists of terms to use with the Text Moderation API. The Text - Screen operation scans your text for profanity, and also compares text against custom and shared blacklists.
- (Exam Topic 2)
You need to build a sentiment analysis solution that will use input data from JSON documents and PDF documents. The JSON documents must be processed in batches and aggregated.
Which storage type should you use for each file type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solution:
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/batch-processing
Does this meet the goal?
Correct Answer:
A
