{"id":18250,"date":"2026-09-22T06:19:55","date_gmt":"2026-09-22T06:19:55","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=18250"},"modified":"2026-09-22T06:19:55","modified_gmt":"2026-09-22T06:19:55","slug":"microsoft-ai-900-practice-test-questions-and-exam-dumps-part4-q61-80","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-ai-900-practice-test-questions-and-exam-dumps-part4-q61-80\/","title":{"rendered":"Microsoft AI-900 Practice Test Questions and Exam Dumps Part4 Q61-80"},"content":{"rendered":"

View Full <\/b>Microsoft AI-900 Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 61<\/b><\/h3>\n

Which Azure AI service is designed to analyze images and provide capabilities such as image tagging, image analysis, and visual content understanding?<\/b><\/p>\n

    \n
  1. Azure AI Speech<\/span><\/li>\n
  2. Azure AI Language<\/span><\/li>\n
  3. Azure AI Vision<\/span><\/li>\n
  4. Azure Machine Learning<\/span><\/li>\n<\/ol>\n

    Correct Answer: 3<\/b><\/p>\n

    Explanation<\/b><\/p>\n

    Azure AI Vision provides computer vision capabilities that enable applications to analyze and interpret visual information. Depending on the capability being used, applications can identify objects, generate tags, extract text, analyze images, and obtain other information from visual content. Azure AI Speech focuses on spoken language, Azure AI Language provides natural language processing capabilities, and Azure Machine Learning supports the broader machine learning lifecycle. When an application needs to understand information contained in images, Azure AI Vision is the relevant Azure service.<\/span><\/p>\n

    Question 62<\/b><\/h3>\n

    A company wants to extract printed text from photographs of receipts so the information can be processed by another application. Which capability should it use?<\/b><\/p>\n

      \n
    1. OCR<\/span><\/li>\n
    2. Clustering<\/span><\/li>\n
    3. Speech synthesis<\/span><\/li>\n
    4. Regression<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1<\/b><\/p>\n

      Explanation<\/b><\/p>\n

      Optical character recognition, or OCR, is used to identify text within images and convert that text into a machine-readable representation. For photographs of receipts, OCR can capture printed information such as item names, amounts, and other visible text. The extracted text can then be processed by additional applications or AI capabilities. Clustering groups similar data, speech synthesis converts text into spoken audio, and regression predicts numerical values. Because the requirement involves extracting visible written content from receipt images, OCR is the appropriate capability.<\/span><\/p>\n

      Question 63<\/b><\/h3>\n

      A security camera image contains several people and vehicles. The application must identify each object and determine its location within the image. Which capability should be selected?<\/b><\/p>\n

        \n
      1. Image classification<\/span><\/li>\n
      2. Object detection<\/span><\/li>\n
      3. Sentiment analysis<\/span><\/li>\n
      4. Translation<\/span><\/li>\n<\/ol>\n

        Correct Answer: 2<\/b><\/p>\n

        Explanation<\/b><\/p>\n

        Object detection identifies individual objects within an image and provides information about where those objects are located. A security application can use this capability to identify multiple people and vehicles and determine their positions, commonly through bounding boxes. Image classification instead assigns categories to an entire image or identifies its overall content without necessarily locating individual objects. Sentiment analysis and translation process language rather than physical objects in images. Therefore, object detection is the appropriate computer vision capability when both object identity and location are required.<\/span><\/p>\n

        Question 64<\/b><\/h3>\n

        An accessibility application receives photographs and needs to generate a short textual description of what each image contains. Which computer vision capability is most appropriate?<\/b><\/p>\n

          \n
        1. Speech recognition<\/span><\/li>\n
        2. Image captioning<\/span><\/li>\n
        3. Classification<\/span><\/li>\n
        4. Anomaly detection<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2<\/b><\/p>\n

          Explanation<\/b><\/p>\n

          Image captioning generates natural-language descriptions of visual content. This capability can help accessibility applications provide users with textual descriptions of images, allowing them to understand important visual information through text or other assistive technologies. Image classification focuses on assigning categories to images, speech recognition processes spoken language, and anomaly detection identifies unusual patterns. Image captioning can combine information about objects and visual context to create a descriptive output. Therefore, it is the most appropriate capability when the application needs to generate a textual description of an image.<\/span><\/p>\n

          Question 65<\/b><\/h3>\n

          Which Azure service provides capabilities for converting speech to text and text to speech?<\/b><\/p>\n

            \n
          1. Azure AI Speech<\/span><\/li>\n
          2. Azure AI Vision<\/span><\/li>\n
          3. Azure AI Language<\/span><\/li>\n
          4. Azure Machine Learning<\/span><\/li>\n<\/ol>\n

            Correct Answer: 1<\/b><\/p>\n

            Explanation<\/b><\/p>\n

            Azure AI Speech provides speech-related capabilities that allow applications to process spoken language and generate speech. These capabilities include speech-to-text, which converts spoken audio into written text, and text-to-speech, which generates spoken audio from written content. Azure AI Vision focuses on visual information, Azure AI Language provides natural language processing capabilities, and Azure Machine Learning provides tools for developing and managing machine learning solutions. Therefore, Azure AI Speech is the appropriate Azure service for applications that need speech recognition or speech synthesis.<\/span><\/p>\n

            Question 66<\/b><\/h3>\n

            A navigation application displays written directions and needs to read those directions aloud to a driver. Which capability should it use?<\/b><\/p>\n

              \n
            1. Speech-to-text<\/span><\/li>\n
            2. Text-to-speech<\/span><\/li>\n
            3. OCR<\/span><\/li>\n
            4. Entity recognition<\/span><\/li>\n<\/ol>\n

              Correct Answer: 2<\/b><\/p>\n

              Explanation<\/b><\/p>\n

              Text-to-speech converts written text into spoken audio. A navigation application can use this capability to turn written directions into speech so that a driver can hear instructions without needing to read the screen. Speech-to-text performs the reverse process by converting spoken language into written text. OCR extracts text from images, while entity recognition identifies entities such as people, organizations, or locations within text. Because the application already has written directions and needs to produce spoken instructions, text-to-speech is the appropriate capability.<\/span><\/p>\n

              Question 67<\/b><\/h3>\n

              A customer support application receives a voice message and must convert the customer’s spoken words into text before analyzing the message. Which capability is required?<\/b><\/p>\n

                \n
              1. Text-to-speech<\/span><\/li>\n
              2. Image classification<\/span><\/li>\n
              3. Speech-to-text<\/span><\/li>\n
              4. Translation<\/span><\/li>\n<\/ol>\n

                Correct Answer: 3<\/b><\/p>\n

                Explanation<\/b><\/p>\n

                Speech-to-text converts spoken audio into written text that can be processed by downstream applications. In this scenario, the customer’s voice message needs to be transcribed before the system can analyze its content. Once the speech has been converted to text, other capabilities can be used to determine sentiment, identify key phrases, recognize entities, or generate a response. Text-to-speech produces audio from written content, image classification processes images, and translation converts language from one language to another. Speech-to-text is therefore the required first step.<\/span><\/p>\n

                Question 68<\/b><\/h3>\n

                A company analyzes thousands of customer comments to determine whether each comment expresses positive, negative, or neutral sentiment. Which Azure AI Language capability is most appropriate?<\/b><\/p>\n

                  \n
                1. Sentiment analysis<\/span><\/li>\n
                2. Entity recognition<\/span><\/li>\n
                3. Key phrase extraction<\/span><\/li>\n
                4. Language detection<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 1<\/b><\/p>\n

                  Explanation<\/b><\/p>\n

                  Sentiment analysis evaluates written text to determine the expressed sentiment, commonly identifying positive, negative, or neutral opinions. Organizations can use this capability to analyze customer reviews, feedback, survey responses, and support conversations at scale. Entity recognition instead identifies entities such as people, places, or organizations. Key phrase extraction identifies important concepts within text, while language detection determines which language a piece of text uses. Since the company specifically wants to identify the emotional or opinion-based tone of customer comments, sentiment analysis is the appropriate capability.<\/span><\/p>\n

                  Question 69<\/b><\/h3>\n

                  An application receives text from users around the world and needs to determine which language each message is written in before processing it. Which capability should it use?<\/b><\/p>\n

                    \n
                  1. Sentiment analysis<\/span><\/li>\n
                  2. Language detection<\/span><\/li>\n
                  3. OCR<\/span><\/li>\n
                  4. Speech synthesis<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 2<\/b><\/p>\n

                    Explanation<\/b><\/p>\n

                    Language detection identifies the language used in a given piece of text. This can be useful in multilingual applications that need to determine which processing path should be used for each message. For example, an application could detect whether a message is written in English, French, Spanish, or another supported language before translating or analyzing it. Sentiment analysis evaluates emotional tone, OCR extracts text from images, and speech synthesis converts text into spoken audio. Therefore, language detection is the most suitable capability for identifying the language of incoming messages.<\/span><\/p>\n

                    Question 70<\/b><\/h3>\n

                    A company wants to identify the most important concepts in long customer feedback messages without manually reviewing every sentence. Which capability can help?<\/b><\/p>\n

                      \n
                    1. Object detection<\/span><\/li>\n
                    2. Speech recognition<\/span><\/li>\n
                    3. Key phrase extraction<\/span><\/li>\n
                    4. Image classification<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 3<\/b><\/p>\n

                      Explanation<\/b><\/p>\n

                      Key phrase extraction identifies significant words and phrases that represent important concepts within a body of text. This can help organizations quickly understand the main topics discussed in customer feedback without manually reviewing every sentence. For example, feedback might contain key phrases related to delivery speed, product quality, customer service, or pricing. Object detection and image classification are computer vision capabilities, while speech recognition processes spoken audio. Key phrase extraction is therefore well suited to applications that need to identify important concepts within large collections of written feedback.<\/span><\/p>\n

                      Question 71<\/b><\/h3>\n

                      A legal application needs to identify names of companies, people, and cities mentioned in contracts. Which language capability should it use?<\/b><\/p>\n

                        \n
                      1. Named entity recognition<\/span><\/li>\n
                      2. Sentiment analysis<\/span><\/li>\n
                      3. Translation<\/span><\/li>\n
                      4. Speech-to-text<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 1<\/b><\/p>\n

                        Explanation<\/b><\/p>\n

                        Named entity recognition identifies and categorizes specific entities within text. In legal documents, it can help identify people, organizations, locations, dates, and other recognized entity types. This can support document analysis and information extraction by turning unstructured text into more structured information. Sentiment analysis focuses on emotional tone, translation converts content between languages, and speech-to-text converts spoken language into written text. Because the legal application needs to locate and categorize names of companies, people, and cities, named entity recognition is the appropriate capability.<\/span><\/p>\n

                        Question 72<\/b><\/h3>\n

                        Which Azure service is most appropriate when an application needs to perform text analysis such as sentiment analysis, language detection, and entity recognition?<\/b><\/p>\n

                          \n
                        1. Azure AI Vision<\/span><\/li>\n
                        2. Azure AI Speech<\/span><\/li>\n
                        3. Azure AI Language<\/span><\/li>\n
                        4. Azure Storage<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 3<\/b><\/p>\n

                          Explanation<\/b><\/p>\n

                          Azure AI Language provides natural language processing capabilities that allow applications to analyze and understand text. Depending on the feature being used, it can support sentiment analysis, language detection, named entity recognition, key phrase extraction, summarization, and other language-related tasks. Azure AI Vision focuses on images, Azure AI Speech handles speech-related workloads, and Azure Storage provides data storage rather than language analysis. Therefore, when an application requires multiple text analysis capabilities, Azure AI Language is the appropriate Azure service.<\/span><\/p>\n

                          Question 73<\/b><\/h3>\n

                          A photo management application must assign labels such as “beach,” “mountain,” “car,” or “food” to uploaded images. Which computer vision task is most appropriate?<\/b><\/p>\n

                            \n
                          1. Image classification<\/span><\/li>\n
                          2. Speech recognition<\/span><\/li>\n
                          3. Regression<\/span><\/li>\n
                          4. Translation<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 1<\/b><\/p>\n

                            Explanation<\/b><\/p>\n

                            Image classification assigns one or more categories or labels to an image based on its visual content. A photo management application can use image classification to identify broad concepts such as beaches, mountains, cars, or food and then use those labels to organize or search the image collection. Speech recognition processes spoken language, regression predicts numerical values, and translation converts content between languages. Since the application needs to categorize images using descriptive labels, image classification is the appropriate computer vision task.<\/span><\/p>\n

                            Question 74<\/b><\/h3>\n

                            A retail store uses cameras to identify each product on a shelf and determine the position of every detected product. Which capability is required?<\/b><\/p>\n

                              \n
                            1. Sentiment analysis<\/span><\/li>\n
                            2. Object detection<\/span><\/li>\n
                            3. Key phrase extraction<\/span><\/li>\n
                            4. Speech synthesis<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 2<\/b><\/p>\n

                              Explanation<\/b><\/p>\n

                              Object detection can identify individual objects in an image and determine where each object appears. In a retail environment, this allows a computer vision system to identify multiple products on a shelf and locate them using bounding boxes or similar spatial information. Sentiment analysis and key phrase extraction are used for text, while speech synthesis converts written content into audio. The requirement involves recognizing physical products and locating each one in a camera image, making object detection the most appropriate computer vision capability.<\/span><\/p>\n

                              Question 75<\/b><\/h3>\n

                              An organization uses an AI model to recommend which applications employees should receive access to. Which responsible AI principle emphasizes that people and organizations remain responsible for decisions involving the AI system?<\/b><\/p>\n

                                \n
                              1. Fairness<\/span><\/li>\n
                              2. Accountability<\/span><\/li>\n
                              3. Inclusiveness<\/span><\/li>\n
                              4. Transparency<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2<\/b><\/p>\n

                                Explanation<\/b><\/p>\n

                                Accountability means that appropriate people and organizations remain responsible for the development, deployment, monitoring, and use of AI systems. Even when an AI model provides recommendations, organizational stakeholders should establish appropriate oversight and remain responsible for decisions made using those recommendations. Fairness focuses on equitable treatment, inclusiveness considers diverse user needs, and transparency concerns making AI behavior and information understandable. When the central concern is determining who remains responsible for an AI system and its outcomes, accountability is the relevant responsible AI principle.<\/span><\/p>\n

                                Question 76<\/b><\/h3>\n

                                A loan prediction model produces substantially different approval outcomes for two groups with similar relevant financial characteristics. Which responsible AI principle should be examined?<\/b><\/p>\n

                                  \n
                                1. Fairness<\/span><\/li>\n
                                2. Reliability<\/span><\/li>\n
                                3. Transparency<\/span><\/li>\n
                                4. Inclusiveness<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 1<\/b><\/p>\n

                                  Explanation<\/b><\/p>\n

                                  Fairness should be considered when an AI system may produce unjustified differences in outcomes between groups. In the loan scenario, substantially different approval outcomes for groups with similar relevant characteristics could indicate that the model needs further evaluation. Organizations can examine training data, model performance, error rates, and other factors to understand whether meaningful disparities exist. Reliability concerns consistent operation, transparency concerns understanding the system and its outputs, and inclusiveness focuses on serving diverse users. The concern described is most directly related to fairness.<\/span><\/p>\n

                                  Question 77<\/b><\/h3>\n

                                  A company wants to provide users with information about how its AI system works, what data it uses, and what limitations its outputs may have. Which responsible AI principle does this support?<\/b><\/p>\n

                                    \n
                                  1. Reliability<\/span><\/li>\n
                                  2. Fairness<\/span><\/li>\n
                                  3. Transparency<\/span><\/li>\n
                                  4. Reinforcement learning<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 3<\/b><\/p>\n

                                    Explanation<\/b><\/p>\n

                                    Transparency involves making information about an AI system understandable to users and stakeholders. This can include explaining how the system operates, what types of data influence its behavior, what its intended purpose is, and what limitations users should consider. Transparency helps people form realistic expectations about AI outputs and can support informed decision-making. Reliability focuses on dependable operation, fairness addresses equitable treatment, and reinforcement learning is a machine learning approach rather than a responsible AI principle. Therefore, the requirement described is primarily related to transparency.<\/span><\/p>\n

                                    Question 78<\/b><\/h3>\n

                                    A public-service AI application is designed so that people with different abilities can interact with it effectively. Which responsible AI principle is most closely associated with this design goal?<\/b><\/p>\n

                                      \n
                                    1. Inclusiveness<\/span><\/li>\n
                                    2. Accountability<\/span><\/li>\n
                                    3. Regression<\/span><\/li>\n
                                    4. Model versioning<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 1<\/b><\/p>\n

                                      Explanation<\/b><\/p>\n

                                      Inclusiveness means designing AI systems so that they can serve people with different abilities, backgrounds, languages, and circumstances. Accessibility is an important consideration because an AI application that works only for a narrow portion of its intended users may fail to meet its broader purpose. Inclusive design can involve considering different interaction methods, accessibility needs, and user characteristics during development and testing. Accountability concerns responsibility for AI systems, regression is a machine learning task, and model versioning manages different model versions. Inclusiveness best matches the described design goal.<\/span><\/p>\n

                                      Question 79<\/b><\/h3>\n

                                      After deployment, an AI model’s input data begins to differ significantly from the data used during training. What should the organization consider doing?<\/b><\/p>\n

                                        \n
                                      1. Stop collecting all new data permanently<\/span><\/li>\n
                                      2. Ignore the change because the model is already deployed<\/span><\/li>\n
                                      3. Monitor the model and investigate whether retraining is needed<\/span><\/li>\n
                                      4. Remove all model evaluation processes<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 3<\/b><\/p>\n

                                        Explanation<\/b><\/p>\n

                                        Changes in input data can affect how well a deployed machine learning model performs. Organizations should monitor production data and model performance to identify significant changes that may reduce accuracy or reliability. If the data distribution has changed substantially, the organization may need to investigate the cause and determine whether additional training or retraining is appropriate. Ignoring the change can allow performance problems to continue unnoticed. Monitoring and periodic evaluation are therefore important parts of managing machine learning models after deployment.<\/span><\/p>\n

                                        Question 80<\/b><\/h3>\n

                                        A company uses a generative AI application to create customer-facing responses. Before releasing the responses automatically, the company evaluates them for accuracy, relevance, harmful content, and adherence to instructions. Why is this evaluation important?<\/b><\/p>\n

                                          \n
                                        1. Generative AI outputs should be assessed before being trusted for important uses<\/span><\/li>\n
                                        2. It guarantees that the model will never generate incorrect content<\/span><\/li>\n
                                        3. It eliminates the need for responsible AI practices<\/span><\/li>\n
                                        4. It prevents the model from generating any text<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 1<\/b><\/p>\n

                                          Explanation<\/b><\/p>\n

                                          Generative AI outputs should be evaluated because generated content can contain inaccurate, irrelevant, unsafe, or otherwise inappropriate information. Reviewing outputs against defined criteria such as accuracy, relevance, safety, and adherence to instructions helps organizations determine whether the application behaves as intended. Evaluation does not guarantee that every future response will be correct, so ongoing monitoring and appropriate safeguards may still be necessary. It also does not prevent the model from generating content. Instead, evaluation provides a structured way to assess whether generated responses are suitable for their intended use.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Microsoft AI-900 Exam Dumps and Practice Test Dumps.   Question 61 Which Azure AI service is designed to analyze images and provide capabilities such as image tagging, image analysis, and visual content understanding? Azure AI Speech Azure AI Language Azure AI Vision Azure Machine Learning Correct Answer: 3 Explanation Azure AI Vision provides […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18250"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=18250"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18250\/revisions"}],"predecessor-version":[{"id":18251,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18250\/revisions\/18251"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=18250"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=18250"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=18250"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}