Microsoft Microsoft Certifications AI-100 Questions & Answers

• Question 41:

  You are developing a mobile application that will perform optical character recognition (OCR) from photos.

  The application will annotate the photos by using metadata, store the photos in Azure Blob storage, and then score the photos by using an Azure Machine Learning model.

  What should you use to process the data?

  A. Azure Event Hubs

  B. Azure Functions

  C. Azure Stream Analytics

  D. Azure Logic Apps

  Correct Answer: A

• Question 42:

  You create an Azure Cognitive Services resource.

  A data scientist needs to call the resource from Azure Logic Apps by using the generic HTTP connector.

  Which two values should you provide to the data scientist? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Endpoint URL

  B. Resource name

  C. Access key

  D. Resource group name

  E. Subscription ID

  Correct Answer: DE

  References: https://social.technet.microsoft.com/wiki/contents/articles/36074.logic-apps-with-azure-cognitive-service.aspx

• Question 43:

  You plan to deploy an AI solution that tracks the behavior of 10 custom mobile apps. Each mobile app has several thousand users.

  You need to recommend a solution for real-time data ingestion for the data originating from the mobile app users.

  Which Microsoft Azure service should you include in the recommendation?

  A. Azure Event Hubs

  B. Azure Service Bus queries

  C. Azure Service Bus topics and subscriptions

  D. Apache Storm on Azure HDInsight

  Correct Answer: A

  References: https://docs.microsoft.com/en-in/azure/event-hubs/event-hubs-about

• Question 44:

  You plan to deploy Azure IoT Edge devices that will each store more than 10,000 images locally and classify the images by using a Custom Vision Service classifier.

  Each image is approximately 5 MB.

  You need to ensure that the images persist on the devices for 14 days.

  What should you use?

  A. The device cache

  B. Azure Blob storage on the IoT Edge devices

  C. Azure Stream Analytics on the IoT Esge devices

  D. Microsoft SQL Server on the IoT Edge devices

  Correct Answer: B

  References: https://docs.microsoft.com/en-us/azure/iot-edge/how-to-store-data-blob

• Question 45:

  Your company is building custom models that integrate into microservices architecture on Azure Kubernetes Services (AKS).

  The model is built by using Python and published to AKS.

  You need to update the model and enable Azure Application Insights for the model.

  What should you use?

  A. the Azure CLI

  B. MLNET Model Builder

  C. the Azure Machine Learning SDK

  D. the Azure portal

  Correct Answer: C

  You can set up Azure Application Insights for Azure Machine Learning. Application Insights gives you the opportunity to monitor:

  1.

  Request rates, response times, and failure rates.

  2.

  Dependency rates, response times, and failure rates.

  3.

  Exceptions.

  Requirements include an Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed.

  References:

  https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/service/how-to-enable-app-insights

• Question 46:

  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 are deploying an Azure Machine Learning model to an Azure Kubernetes Service (AKS) container.

  You need to monitor the scoring accuracy of each run of the model.

  Solution: You modify the Config.json file.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Instead update the manifest file.

  Reference: https://azure.github.io/learnAnalytics-UsingAzureMachineLearningforAIWorkloads/lab07-deploying_a_scoring_service_to_aks/0_README.html

• Question 47:

  You need to build an API pipeline that analyzes streaming data. The pipeline will perform the following:

  1.

  Visual text recognition

  2.

  Audio transcription

  3.

  Sentiment analysis

  4.

  Face detection

  Which Azure Cognitive Services should you use in the pipeline?

  A. Custom Speech Service

  B. Face API

  C. Text Analytics

  D. Video Indexer

  Correct Answer: D

  Azure Video Indexer is a cloud application built on Azure Media Analytics, Azure Search, Cognitive Services (such as the Face API, Microsoft Translator, the Computer Vision API, and Custom Speech Service). It enables you to extract the insights from your videos using Video Indexer video and audio models described below:

  1.

  Visual text recognition (OCR): Extracts text that is visually displayed in the video.

  2.

  Audio transcription: Converts speech to text in 12 languages and allows extensions.

  3.

  Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.

  4.

  Face detection: Detects and groups faces appearing in the video.

  References: https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-overview

• Question 48:

  You design an AI solution that uses an Azure Stream Analytics job to process data from an Azure IoT hub. The IoT hub receives time series data from thousands of IoT devices at a factory.

  The job outputs millions of messages per second. Different applications consume the messages as they are available. The messages must be purged.

  You need to choose an output type for the job.

  What is the best output type to achieve the goal? More than one answer choice may achieve the goal.

  A. Azure Event Hubs

  B. Azure SQL Database

  C. Azure Blob storage

  D. Azure Cosmos DB

  Correct Answer: D

  Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low-latency queries on unstructured JSON data.

  References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-documentdb-output

• Question 49:

  You need to deploy cognitive search. You provision an Azure Search service.

  What should you do next?

  A. Search by using the .NET SDK.

  B. Load data.

  C. Search by using the REST API.

  D. Create an index.

  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

• Question 50:

  You need to design an application that will analyze real-time data from financial feeds.

  The data will be ingested into Azure IoT Hub. The data must be processed as quickly as possible in the order in which it is ingested.

  Which service should you include in the design?

  A. Azure Data Factory

  B. Azure Queue storage

  C. Azure Stream Analytics

  D. Azure Notification Hubs

  E. Apache Kafka

  F. Azure Event Hubs

  Correct Answer: C

  Stream processing can be handled by Azure Stream Analytics. Azure Stream Analytics can run perpetual queries against an unbounded stream of data. These queries consume streams of data from storage or message brokers, filter and aggregate the data based on temporal windows, and write the results to sinks such as storage, databases, or directly to reports in Power BI. Stream Analytics uses a SQL-based query language that supports temporal and geospatial constructs, and can be extended using JavaScript.

  Incorrect Answers:

  E: Apache Kafka is used for ingestion, not for stream processing.

  F: Azure Event Hubs is used for ingestion, not for stream processing.

  Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/real-time-processing