2025 HIGH PASS-RATE NEW AI-900 TEST DISCOUNT HELP YOU PASS AI-900 EASILY

2025 High Pass-Rate New AI-900 Test Discount Help You Pass AI-900 Easily

2025 High Pass-Rate New AI-900 Test Discount Help You Pass AI-900 Easily

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Microsoft AI-900 Certification Exam covers a range of topics related to AI, including machine learning, natural language processing, computer vision, and cognitive services. AI-900 exam is designed to test the candidate's understanding of the core concepts of AI and how they can be applied to real-world scenarios. Candidates who pass the exam demonstrate their ability to use Microsoft Azure AI services to build intelligent solutions.

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Microsoft Azure AI Fundamentals Sample Questions (Q155-Q160):

NEW QUESTION # 155
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation

To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint.
Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy


NEW QUESTION # 156
Which metric can you use to evaluate a classification model?

  • A. true positive rate
  • B. root mean squared error (RMSE)
  • C. mean absolute error (MAE)
  • D. coefficient of determination (R2)

Answer: A

Explanation:
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification


NEW QUESTION # 157
You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Education Level
  • B. Age
  • C. Income Range
  • D. Last Name
  • E. First Name

Answer: A,B

Explanation:
Explanation
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.


NEW QUESTION # 158
When training a model, why should you randomly split the rows into separate subsets?

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

Answer: A

Explanation:
Explanation
The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using "new" examples from the held-out datasets (validation and test datasets) to estimate the model's accuracy in classifying new data.
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,- A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%


NEW QUESTION # 159
You need to determine the location of cars in an image so that you can estimate the distance between the cars.
Which type of computer vision should you use?

  • A. optical character recognition (OCR)
  • B. object detection
  • C. image classification
  • D. face detection

Answer: B

Explanation:
Explanation
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection


NEW QUESTION # 160
......

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