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Exam Details

The Microsoft AI-900 exam is the only qualifying test that leads to the Microsoft Certified: Azure AI Fundamentals certification. It measures one’s knowledge of AI considerations & workloads, features of NLP workloads on Azure, and basic principles of ML on Azure. The candidates can choose to take this exam in English, French, Simplified Chinese, Japanese, German, Spanish, or Korean. It contains 40-60 questions covering different types, including case studies, drag and drop, active screen, build list, and multiple choice. You can schedule your test with Certiport or Pearson VUE. However, it is required to pay the fee of $99 before scheduling it.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

AI-900 certification is more and more important for this area, but the exam is not easy for many candidates. Our AI-900 practice materials make it easier to prepare exam with a variety of high quality functions. Their quality function is observably clear once you download them. We have three kinds of AI-900 practice materials moderately priced for your reference. All these three types of AI-900 practice materials win great support around the world and all popular according to their availability of goods, prices and other term you can think of.

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Requisites of Microsoft AI-900 Exam

In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:

  • AZ-100: Fundamentals of Azure
  • AZ-200: Azure Fundamentals Assessment

Learning Path

All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:

  • Microsoft Azure AI Fundamentals

    This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.

  • Get Started with Artificial Intelligence

    The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.

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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Supervised vs unsupervised learning
    • 2. Training and validation concepts
      Topic 2: Features of computer vision workloads on Azure15-20%- Computer vision solutions
      • 1. Object detection
        • 2. Image classification
          • 3. OCR and image analysis
            Topic 3: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
            • 1. Common AI workloads
              • 2. Responsible AI principles
                Topic 4: Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
                • 1. Language modeling and translation
                  • 2. Key phrase extraction
                    • 3. Sentiment analysis

                      Microsoft Azure AI Fundamentals Sample Questions:

                      What is an example of unsupervised machine learning?

                      • A. clustering
                      • B. classification
                      • C. regression
                      Reveal Solution  Discussion  0

                      Correct Answer: A  🗳️

                      Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).

                      To complete the sentence, select the appropriate option in the answer area.

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:
                      Confidence.
                      According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," the confidence score represents the calculated probability that a model's prediction is correct. In image classification, when an AI model analyzes an image and assigns it to a specific category, it also produces a confidence value-a numerical probability (usually between 0 and 1) indicating how certain the model is about its prediction.
                      For example, if an image classification model identifies an image as a "cat" with a confidence of 0.92, it means the model is 92% certain that the image depicts a cat. The confidence value helps developers and users understand the model's certainty level about its classification output.
                      Microsoft Learn emphasizes that in Azure Cognitive Services-such as the Custom Vision Service-each prediction result includes both the predicted label (class) and a confidence score. These confidence scores are essential for evaluating model performance and determining thresholds for automated decisions (e.g., accepting predictions only above a 0.8 probability).
                      Let's evaluate the other options:
                      * Accuracy: This is an overall performance metric measuring the percentage of correct predictions across the dataset, not a probability for a single prediction.
                      * Root Mean Square Error (RMSE): This is a metric for regression models, not classification tasks. It measures average error magnitude between predicted and actual values.
                      * Sentiment: This is a type of prediction (positive, negative, neutral) in text analysis, not a probability metric.
                      Therefore, based on Microsoft's AI-900 training materials and Azure Cognitive Services documentation, the calculated probability of a correct image classification is called Confidence, which expresses how sure the model is about its prediction for a specific input.

                      You are processing photos of runners in a race.
                      You need to read the numbers on the runners' shirts to identity the runners in the photos.
                      Which type of computer vision should you use?

                      • A. object detection
                      • B. semantic segmentation
                      • C. optical character recognition (OCR)
                      • D. facial recognition
                      Reveal Solution  Discussion  0

                      Correct Answer: C  🗳️

                      Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).

                      For each of the following statements, select Yes if the statement is true. Otherwise, select No.
                      NOTE: Each correct selection is worth one point.

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:

                      This question tests understanding of Microsoft's six guiding principles for Responsible AI, which are:
                      fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles, as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Responsible AI module, help ensure that AI systems are developed and used ethically and responsibly.
                      * Transparency - Yes:Transparency means users should understand how and why an AI system makes certain decisions. Providing an explanation of the outcome of a credit loan application clearly supports transparency because it helps customers know the reasoning behind approval or rejection. According to Microsoft Learn, transparency ensures that "AI systems are understandable by users and stakeholders," especially in sensitive applications such as finance and credit scoring. Thus, the first statement is Yes.
                      * Reliability and Safety - Yes:The reliability and safety principle ensures AI systems perform consistently, safely, and as intended, even in complex or high-risk environments. A triage bot that prioritizes insurance claims based on injury type aligns with this principle-it must be accurate, dependable, and safe to ensure claims are processed correctly and not influenced by errors or faulty algorithms. Microsoft teaches that AI should be "reliable under expected and unexpected conditions" to prevent harm or misjudgment. Therefore, this statement is Yes.
                      * Inclusiveness - No:Inclusiveness focuses on ensuring AI systems empower and benefit all users, especially those with different abilities or backgrounds. Offering an AI solution at different prices across sales territories is a business decision, not an ethical or inclusiveness principle issue. It does not relate to accessibility or equal participation of diverse users. Therefore, this final statement is No.

                      When training a model, why should you randomly split the rows into separate subsets?

                      • A. to train the model twice to attain better accuracy
                      • B. to train multiple models simultaneously to attain better performance
                      • C. to test the model by using data that was not used to train the model
                      Reveal Solution  Discussion  0

                      Correct Answer: C  🗳️

                      Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).

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