Free PDF AI-900 - High Pass-Rate Microsoft Azure AI Fundamentals Official Practice Test

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Describing AI-900 Exam Facts and Topics

In general, AI-900 is a computer-based exam that’s available in English, Chinese, Japanese, Spanish, French, German, and Korean languages. It costs $99 for every attempt and can be scheduled with Certiport or Pearson VUE. Recently, the AI-900 exam curriculum has been updated to reflect the following skills as contained in the official study guide:

Defining features of conversational AI workloads on Azure (15-20%).
Designing features of NLP (Natural Language Processing) workloads on Azure (15-20%);
Detailing Artificial Intelligence considerations and workloads (15-20%);
Explaining features of computer vision workloads on Azure (15-20%);
Describing fundamental principles of machine learning on Azure (30-35%);

Who should take the AI-900: Microsoft Azure AI Fundamentals Exam

High level of difficulty and many challenging questions.Identify the different types of AI models.AI design choosing tools. Who should take the exam? Artificial intelligence. Describe the strengths and weaknesses for artificial intelligence.

The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:

Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.
Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.
Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.

Microsoft Azure AI Fundamentals Sample Questions (Q60-Q65):

NEW QUESTION # 60
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features

NEW QUESTION # 61
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NEW QUESTION # 62
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:
Explanation

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure

NEW QUESTION # 63
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
This is an example of which Microsoft guiding principle for responsible AI?

A. inclusiveness
B. transparency
C. fairness
D. privacy and security

Answer: B

Explanation:
Explanation
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai

NEW QUESTION # 64
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation

NEW QUESTION # 65
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