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Microsoft AI-900 certification exam covers various topics related to AI, including the fundamentals of AI, machine learning, computer vision, natural language processing, and conversational AI. Candidates who pass AI-900 exam will demonstrate their understanding of these topics and their ability to apply them in real-world scenarios. The AI-900 exam is an excellent starting point for individuals who want to pursue a career in AI and machine learning.
The AI-900 Exam is an entry-level certification that does not require any prerequisites. However, it is recommended that you have some basic knowledge of cloud computing and programming concepts before taking AI-900 exam. AI-900 exam is available in multiple languages and can be taken online or at a testing center.
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The Microsoft AI-900 Exam consists of multiple-choice questions, and individuals have to score a minimum of 700 points out of 1000 to pass the exam. AI-900 exam can be taken online, and individuals can prepare for the exam by taking online courses or attending training sessions. Microsoft offers various training resources, including online courses, practice tests, and instructor-led training, to help individuals prepare for the exam.
NEW QUESTION # 70
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 71
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
NEW QUESTION # 72
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 73
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://machinelearningmastery.com/difference-test-validation-datasets/
NEW QUESTION # 74
What is a form of unsupervised machine learning?
Answer: D
Explanation:
As outlined in the AI-900 study guide and Microsoft Learn's "Explore fundamental principles of machine learning" module, clustering is a core example of unsupervised machine learning.
In unsupervised learning, the model is trained on data without labeled outcomes. The goal is to discover patterns or groupings naturally present in the data. Clustering algorithms, such as K-means, DBSCAN, or Hierarchical clustering, analyze similarities among data points and group them into clusters. For example, clustering can group customers by purchasing behavior or segment products by shared characteristics - all without predefined labels.
Supervised learning, by contrast, uses labeled data (input-output pairs) to train a model that predicts outcomes. This includes:
* A. Multiclass classification - Predicts more than two categories (e.g., classifying images as dog, cat, or bird).
* C. Binary classification - Predicts two categories (e.g., spam vs. not spam).
* D. Regression - Predicts continuous numeric values (e.g., price prediction).
Therefore, the only option representing unsupervised learning is clustering, which enables data discovery without predefined labels.
NEW QUESTION # 75
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