AI-900 Updated Dumps, AI-900 Pass Test

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Microsoft AI-900 Exam Syllabus Topics:

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

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                      For the Microsoft Azure AI Fundamentals (AI-900) web-based practice exam no special software installation is required. because it is a browser-based AI-900 practice test. The web-based AI-900 practice exam works on all operating systems like Mac, Linux, iOS, Android, and Windows. In the same way, IE, Firefox, Opera and Safari, and all the major browsers support the web-based Microsoft AI-900 Practice Test. So it requires no special plugins. The web-based AI-900 practice exam software is genuine, authentic, and real so feel free to start your practice instantly with AI-900 practice test.

                      Microsoft Azure AI Fundamentals Sample Questions (Q226-Q231):

                      NEW QUESTION # 226
                      Select the answer that correctly completes the sentence.

                      Answer:

                      Explanation:

                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI," Fairness is one of Microsoft's six core principles of Responsible AI. The principle of fairness ensures that AI systems treat all individuals and groups equitably, and that the models do not produce biased or discriminatory outcomes.
                      Bias in AI systems can occur when training data reflects existing prejudices, inequalities, or imbalances. For example, if a dataset used for a hiring model underrepresents a certain demographic group, the AI system might produce unfair recommendations. Microsoft emphasizes that AI should not reflect or reinforce bias and that developers must actively design, test, and monitor models to mitigate unfairness.
                      Microsoft's Six Responsible AI Principles:
                      * Fairness - AI systems should treat everyone equally and avoid bias.
                      * Reliability and safety - AI systems must operate as intended even under unexpected conditions.
                      * Privacy and security - AI must protect personal and business data.
                      * Inclusiveness - AI should empower all people and be accessible to diverse users.
                      * Transparency - AI systems should be understandable and their decisions explainable.
                      * Accountability - Humans should be accountable for AI system outcomes.
                      The other options do not fit this context:
                      * Accountability ensures human responsibility for AI decisions.
                      * Inclusiveness focuses on accessibility and empowering all users.
                      * Transparency relates to making AI systems understandable.
                      Therefore, the correct answer is fairness, as it directly addresses the principle that AI systems should NOT reflect biases from the datasets used to train them.


                      NEW QUESTION # 227
                      Select the answer that correctly completes the sentence

                      Answer:

                      Explanation:

                      Explanation:


                      NEW QUESTION # 228
                      What should you implement to identify hateful responses returned by a generative Al solution?

                      Answer: A

                      Explanation:
                      When using generative AI models such as those in Azure OpenAI, it's critical to monitor and control the model's outputs to prevent the generation of harmful, hateful, or unsafe content. According to Microsoft's Responsible AI guidelines and Azure OpenAI safety systems, this is achieved through content filtering mechanisms.
                      Content filtering automatically analyzes generated text and blocks or flags responses containing hate speech, harassment, sexual content, or violent language. Azure OpenAI applies these filters by default through its built-in content moderation system, which categorizes responses into safety levels (safe, review, block).
                      Option review:
                      * A. Prompt engineering: Helps guide responses but does not detect hate content automatically.
                      * B. Abuse monitoring: Refers to manual or post-processing review, not real-time automated filtering.
                      * C. Content filtering: # Correct - actively detects and prevents hateful or unsafe responses.
                      * D. Fine-tuning: Adjusts model behavior using additional training data but doesn't ensure safety monitoring.


                      NEW QUESTION # 229
                      Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
                      To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
                      NOTE: Each correct selection is worth one point.

                      Answer:

                      Explanation:


                      NEW QUESTION # 230
                      Select the answer that correctly completes the sentence.

                      Answer:

                      Explanation:

                      Explanation:


                      NEW QUESTION # 231
                      ......

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