Free PDF Microsoft - AI-900 Pass-Sure Popular Exams

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The Microsoft AI-900 exam covers a wide range of topics, including the principles of AI, machine learning, computer vision, natural language processing (NLP), and conversational AI. It also includes an overview of Microsoft Azure AI services such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. Passing the AI-900 Exam validates one's knowledge and skills in these areas and provides a solid foundation for pursuing other Azure certifications related to AI and machine learning.

Microsoft Azure AI Fundamentals Sample Questions (Q317-Q322):

NEW QUESTION # 317
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:

Explanation:

This question evaluates understanding of clustering-an unsupervised learning technique explained in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore fundamental principles of machine learning." Clustering involves finding natural groupings within data without prior knowledge of output labels. The algorithm identifies similarities among data points and groups them accordingly, with each group (or cluster) containing items that are more similar to each other than to those in other groups.
* Organizing documents into groups based on similarities of the text contained in the documents # YesThis is a classic clustering application. In text analytics or natural language processing (NLP), clustering algorithms such as K-means or hierarchical clustering are used to group documents with similar content or topics. According to Microsoft Learn, "clustering identifies relationships in data and groups items that share common characteristics." Therefore, organizing text documents based on content similarity is a textbook example of clustering.
* Grouping similar patients based on symptoms and diagnostic test results # YesThis is another example of clustering. In healthcare analytics, clustering can be used to segment patients with similar health patterns or risks. The study guide emphasizes that clustering can "discover natural groupings in data such as customers with similar buying patterns or patients with similar clinical results." Thus, this task correctly describes unsupervised clustering because it does not involve predicting a known outcome but grouping based on similarity.
* Predicting whether a person will develop mild, moderate, or severe allergy symptoms based on pollen count # NoThis is a classification problem, not clustering. Classification is a supervised learning technique where the model is trained with labeled data to predict predefined categories (in this case, mild, moderate, or severe). Microsoft Learn clearly distinguishes between clustering (discovering hidden patterns) and classification (predicting predefined categories).

NEW QUESTION # 318
Match the computer vision service to the appropriate Al workload.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Explanation:
A close-up of a document Description automatically generated

NEW QUESTION # 319
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 # 320
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 # 321
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 test the model by using data that was not used to train the model
C. to train multiple models simultaneously to attain better performance

Answer: B

Explanation:
When training a machine learning model, it is standard practice to randomly split the dataset into training and testing subsets. The purpose of this is to evaluate how well the model generalizes to unseen data. According to the AI-900 study guide and Microsoft Learn module "Split data for training and evaluation", this ensures that the model is trained on one portion of the data (training set) and evaluated on another (test or validation set).
The correct answer is C. to test the model by using data that was not used to train the model.
Random splitting prevents data leakage and overfitting, which occur when a model memorizes patterns from the training data instead of learning generalizable relationships. By testing on unseen data, developers can assess true performance, ensuring that predictions will be accurate on future, real-world data.
Options A and B are incorrect because:
* A. Train the model twice does not improve accuracy; model accuracy depends on data quality, feature engineering, and algorithm choice.
* B. Train multiple models simultaneously refers to model comparison, not the purpose of splitting data.
Thus, the correct reasoning is that random splitting provides a reliable estimate of the model's predictive power on new data.
Reference:Microsoft Learn - Split data for training and evaluation in Azure Machine Learning designer

NEW QUESTION # 322
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Free PDF Microsoft - AI-900 Pass-Sure Popular Exams

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Microsoft Azure AI Fundamentals Sample Questions (Q317-Q322):

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