Microsoft Microsoft Certifications AI-100 Questions & Answers

• Question 141:

  You company's developers have created an Azure Data Factory pipeline that moves data from an on-premises server to Azure Storage. The pipeline consumes

  Azure Cognitive Services APIs.

  You need to deploy the pipeline. Your solution must minimize custom code.

  You use Self-hosted Integration Runtime to move data to the cloud and Azure Logic Apps to consume Cognitive Services APIs.

  Does this action accomplish your objective?

  A. Yes, it does

  B. No, it does not

  Correct Answer: A

  Using a Self-hosted Integration Runtime in Azure Data Factory allows you to move data from an on-premises server to Azure Storage. Azure Logic Apps can be used to consume Azure Cognitive Services APIs by leveraging the built-in connectors and actions available for interacting with Cognitive Services.

  With Azure Logic Apps, you can easily create workflows and utilize the Logic Apps Cognitive Services connectors to integrate with Azure Cognitive Services APIs, such as Text Analytics, Computer Vision, or Language Understanding (LUIS). This allows you to incorporate cognitive capabilities into your data pipeline without the need for significant custom code.

  Therefore, by using Self-hosted Integration Runtime for data movement and Azure Logic Apps for consuming Cognitive Services APIs, you can deploy the pipeline and accomplish your objective of minimizing custom code.

• Question 142:

  You are developing an application that uses the Computer Vision API.

  Your application will perform the following steps:

  Take data from an on-premises database and load it to an Azure Blob storage account.

  Connect to an Azure Machine Learning service.

  You need to orchestrate the workflow.

  What should you use?

  A. Azure Kubernetes Service (AKS)

  B. Azure Pipelines

  C. Azure Data Factory

  D. An Azure HDInsight cluster

  E. Azure Data Lake

  Correct Answer: C

  Azure Data Factory is a fully managed data integration service that allows you to orchestrate and automate data movement and data transformation workflows. It provides a visual interface for building complex data pipelines and supports

  various data sources and destinations, including on-premises databases and Azure Blob storage. With Azure Data Factory, you can easily move data from your on-premises database to Azure Blob storage and then connect to Azure Machine

  Learning service for further processing using the Computer Vision API.

  Reference:

  https://azure.microsoft.com/en-us/services/data-factory/

• Question 143:

  You have deployed several Azure IoT Edge devices for an AI solution. The Azure IoT Edge devices generate measurement data from temperature sensors.

  You need a solution to process the sensor data. Your solution must be able to write configuration changes back to the devices.

  You make use of Microsoft Azure Event Hub.

  Does this action accomplish your objective?

  A. Yes, it does

  B. No, it does not

  Correct Answer: B

  Use Microsoft Azure IoT Hub instead.

  Reference:

  https://azure.microsoft.com/en-us/resources/samples/functions-js-iot-hub-processing/

• Question 144:

  You are developing a bot for an ecommerce application. The bot will support five languages.

  The bot will use Language Understanding (LUIS) to detect the language of the customer, and QnA Maker to answer common customer questions. LUIS supports all the languages.

  You need to determine the minimum number of Azure resources that you must create for the bot.

  You create five instances of QnA Maker and five instances Language Understanding (LUIS).

  Does this action accomplish your objective?

  A. Yes, it does

  B. No, it does not

  Correct Answer: A

  You need to have a new QnA Maker resource for each language.

  If LUIS supports all the languages, you develop a LUIS app for each language. Each LUIS app has a unique app ID, and endpoint log. If you need to provide language understanding for a language LUIS does not support, you can use

  Microsoft Translator API to translate the utterance into a supported language, submit the utterance to the LUIS endpoint, and receive the resulting scores.

  Reference:

  https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/language-support https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-language-support

• Question 145:

  You are designing an AI system for your company. Your system will consume several Apache Kafka data streams.

  You want your system to be able to process the data streams at scale and in real-time.

  Which of the following actions should you take?

  A. Make use of Azure HDInsight with Apache HBase

  B. Make use of Azure HDInsight with Apache Spark

  C. Make use of Azure HDInsight with Apache Storm

  D. Make use of Azure HDInsight with Microsoft Machine Learning Server

  Correct Answer: C

  Azure HDInsight is a cloud-based big data analytics service that allows you to process and analyze large volumes of data. It supports various open-source big data technologies, including Apache Kafka, Apache HBase, Apache Spark, and

  Apache Storm.

  Apache Storm is a distributed real-time processing system designed for processing high-velocity streaming data. It provides a scalable and fault-tolerant platform for processing data streams in real-time. By using Azure HDInsight with Apache

  Storm, you can effectively process and analyze your Apache Kafka data streams at scale and in real-time.

  Reference:

  https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-streaming-at-scale-overview https://docs.microsoft.com/en-us/azure/hdinsight/storm/apache-storm-overview

• Question 146:

  Your company is developing an AI solution that will identify inappropriate text in multiple languages.

  You need to implement a Cognitive Services API that meets this requirement.

  You use the Azure Content Moderator API to identify inappropriate text.

  Does this action accomplish your objective?

  A. Yes, it does

  B. No, it does not

  Correct Answer: A

  The Azure Content Moderator API is a cognitive service that checks text, image, and video content for material that is potentially offensive, risky, or otherwise undesirable. When such material is found, the service applies appropriate labels

  (flags) to the content. Your app can then handle flagged content in order to comply with regulations or maintain the intended environment for users.

  Reference:

  https://docs.microsoft.com/bs-latn-ba/azure/cognitive-services/content-moderator/overview

• Question 147:

  Your company is developing an AI solution that will identify inappropriate text in multiple languages.

  You need to implement a Cognitive Services API that meets this requirement.

  You use Language Understanding (LUIS) to identify inappropriate text.

  Does this action accomplish your objective?

  A. Yes, it does

  B. No, it does not

  Correct Answer: B

  Language Understanding (LUIS) is designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. LUIS

  integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot.

  Use Content Moderation instead.

  Reference:

  https://www.luis.ai/home

  https://docs.microsoft.com/bs-latn-ba/azure/cognitive-services/content-moderator/overview

• Question 148:

  Your company is developing an AI solution that will identify inappropriate text in multiple languages.

  You need to implement a Cognitive Services API that meets this requirement.

  You use the Text Analytics API to identify inappropriate text.

  Does this action accomplish your objective?

  A. Yes, it does

  B. No, it does not

  Correct Answer: A

  The Text Analytics API includes a feature called "Content Moderation" that can be used to identify and flag inappropriate or offensive text in multiple languages. By utilizing this feature of the Text Analytics API, you can meet the requirement of identifying inappropriate text in multiple languages.

• Question 149:

  You are developing an app that will analyze sensitive data from global users.

  Your app must adhere the following compliance policies:

  The app must not store data in the cloud.

  The app not use services in the cloud to process the data.

  Which of the following actions should you take?

  A. Make use of Azure Machine Learning Studio

  B. Make use of Docker containers for the Text Analytics

  C. Make use of a Text Analytics container deployed to Azure Kubernetes Service D. Make use of Microsoft Machine Learning (MML) for Apache Spark

  Correct Answer: D

  https://github.com/MicrosoftDocs/azure-docs/blob/ccf49761e4aefed30d723805f4f09e753615fb09/articles/cognitive-services/cognitive-services-container-support.md

• Question 150:

  You are developing an AI application for a virtual classroom.

  Your application must perform the following tasks:

  Capture audio and video data of the students in the virtual classroom.

  Identify each student in the virtual classroom for attendance purposes.

  Alert the teacher when a student's facial expression indicates the student having difficulty.

  Which two of the following actions should you take? Each correct selection is part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Make use of the Face API

  B. Make use of the Computer Vision API

  C. Make use of the QnA Maker

  D. Make use of the Video Indexer API

  E. Make use of the Custom Speech Service

  Correct Answer: AD

  A. Make use of the Face API: The Face API can be used to detect and identify individuals in images and videos. In this case, it can be utilized to identify each student in the virtual classroom for attendance purposes based on their facial features. This API can help capture the audio and video data of the students by recognizing their faces and linking them to their identities.

  D. Make use of the Video Indexer API: The Video Indexer API enables the analysis and extraction of insights from video content. It can be employed to capture the audio and video data of the students in the virtual classroom. Additionally, the Video Indexer API offers capabilities for facial emotion recognition, which can help identify when a student's facial expression indicates difficulty. This can trigger an alert for the teacher, allowing timely intervention.