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[General] Pass Guaranteed 2026 Newest AI-900: New Microsoft Azure AI Fundamentals Braindum

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【General】 Pass Guaranteed 2026 Newest AI-900: New Microsoft Azure AI Fundamentals Braindum

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Career OpportunitiesAs you remember, the Microsoft Certified: Azure AI Fundamentals validation is the fundamental certification designed for beginners with both technical and non-technical backgrounds. It is considered a good path to follow if you're interested in starting a career in the field of AI and ML. Below are some of the jobs that you can get your hand on through this certification. These jobs come with decent annual salaries as covered by Payscale.com:
  • AI Engineer: $76k;
  • Data Scientist: $67k.
  • Developer: $50k;
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.
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Microsoft AI-900 Exam covers various AI concepts, including machine learning, natural language processing, computer vision, and conversational AI. It also tests knowledge of Azure AI services such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. Candidates are expected to have a basic understanding of programming languages, data analysis, and cloud computing concepts.
Microsoft Azure AI Fundamentals Sample Questions (Q219-Q224):NEW QUESTION # 219
Select the answer that correctly completes the sentence.

Answer:
Explanation:

Explanation:
"features."
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe fundamental principles of machine learning on Azure," in a machine learning model, the data used as inputs are known as features, while the data that represents the output or target prediction is known as the label.
Features are measurable attributes or properties of the data used by a model to learn patterns and make predictions. They are also referred to as independent variables because they influence the result that the model tries to predict. For example, in a machine learning model that predicts house prices:
* Features might include square footage, location, and number of bedrooms, while
* The label would be the house price (the value being predicted).
In the context of Azure Machine Learning, during model training, features are passed into the algorithm as input variables (X-values), and the label is the corresponding output (Y-value). The model then learns the relationship between the features and the label.
Let's review the incorrect options:
* Functions: These are mathematical operations or relationships used inside algorithms, not the input data itself.
* Labels: These are the outputs or results that the model predicts, not the inputs.
* Instances: These refer to individual data records or rows in the dataset, not the input fields themselves.
Hence, in any supervised or unsupervised learning process, the input data (independent variables) are called features, and the model uses them to predict labels (dependent variables).

NEW QUESTION # 220
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://docs.microsoft.com/en-us ... rted-build-detector

NEW QUESTION # 221
What should you do to reduce the number of false positives produced by a machine learning classification model?
  • A. Increase the number of training iterations.
  • B. Modify the threshold value in favor of false positives.
  • C. Include test data in the training data.
  • D. Modify the threshold value in favor of false negatives.
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of machine learning on Azure", a classification model outputs a probability score representing how likely each input belongs to a particular class. To decide whether a prediction is "positive" or "negative," the model applies a threshold (often defaulted to 0.5). Adjusting this threshold directly affects the balance between false positives and false negatives.
* A false positive occurs when the model incorrectly predicts a positive outcome (for example, predicting that a patient has a disease when they do not).
* A false negative occurs when the model fails to predict a true positive (for example, predicting that a patient does not have a disease when they actually do).
To reduce false positives, you must make the model less likely to classify borderline cases as positive. This is done by increasing the decision threshold, thereby favoring false negatives (because the model will only classify a case as positive when the prediction confidence is very high). In other words, by moving the threshold upward, you tighten the model's standard for what qualifies as a "positive" prediction, reducing incorrect positives.
Let's review why other options are incorrect:
* A. Include test data in training data: This contaminates your dataset and causes overfitting, which leads to unreliable performance metrics.
* B. Increase the number of training iterations: This may improve learning but doesn't specifically target false positives.
* C. Modify the threshold in favor of false positives: That would increase, not reduce, false positives.
Therefore, the correct step to reduce false positives is to adjust the threshold in favor of false negatives, making the model more conservative when labeling a case as positive - hence, answer: D.

NEW QUESTION # 222
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:


NEW QUESTION # 223
To complete the sentence, select the appropriate option in the answer area.

Answer:
Explanation:

Reference:
https://docs.microsoft.com/en-us ... language-processing

NEW QUESTION # 224
......
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