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AI-900 Real Exam Questions

Microsoft Azure AI Fundamentals

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Question 1 Hotspot

HOTSPOT

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

Question diagram
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Explanation

Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

Box 2: No

Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment.

Box 4: No

Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the
target metric you specify.
The label is the column you want to predict.

References:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features

Question 2 Single choice

Which statement is an example of a Microsoft responsible AI principle?

  1. A

    AI systems must use only publicly available data

  2. B

    AI systems must be transparent and inclusive

  3. C

    AI systems must keep personal details public

  4. D

    AI systems must protect the interests of the company

Show answer and explanation

Correct answer: B

Question 3 Single choice

You are building an Al-based loan approval app.

You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.

This is an example of which Microsoft responsible Al principle?

  1. A

    fairness

  2. B

    inclusiveness

  3. C

    transparency

  4. D

    accountability

Show answer and explanation

Correct answer: C

Question 4 Single choice

You plan to use Azure Machine Learning Studio and automated machine learning (automated ML) to build and train a model

What should you create first?

  1. A

    a Jupyter notebook

  2. B

    a Machine Learning workspace

  3. C

    a registered dataset

  4. D

    a Machine Learning designer pipeline

Show answer and explanation

Correct answer: B

Question 5 Drag & drop

DRAG DROP

You plan to use Azure Cognitive Services to develop a voice controlled personal assistant app.

Match the Azure Cognitive Services to the appropriate tasks.

To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.

NOTE: Each correct selection is worth one point.

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Show answer and explanation
Correct answer diagram
Explanation

Box 1: Speech
The Speech service provides speech-to-text and text-to-speech capabilities with an Azure Speech resource. You can transcribe speech to text with high accuracy, produce natural-sounding text-to-speech voices, translate spoken audio, and use speaker recognition during conversations.

Box 2: Language service
Build applications with conversational language understanding, a Cognitive Service for Language feature that understands natural language to interpret user goals and extracts key information from conversational phrases. Create multilingual, customizable intent classification and entity extraction models for your domain-specific keywords or phrases across 96 languages.

Box 3: Speech

Incorrect:
Not Translator text: Text translation is a cloud-based REST API feature of the Translator service that uses neural machine translation technology to enable quick and accurate source-to-target text translation in real time across all supported languages.

References:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/overview
https://azure.microsoft.com/en-us/services/cognitive-services/conversational-language-understanding/
https://docs.microsoft.com/en-us/azure/cognitive-services/translator/text-translation-overview

Question 6 Single choice

You need to identify street names based on street signs in photographs.

Which type of computer vision should you use?

  1. A

    object detection

  2. B

    optical character recognition (OCR)

  3. C

    image classification

  4. D

    facial recognition

Show answer and explanation

Correct answer: B

Question 7 Single choice

A retailer wants to automatically categorize product images into groups such as shoes, shirts, and accessories.

Which computer vision capability should be used?

  1. A

    Image classification

  2. B

    Object detection

  3. C

    OCR

  4. D

    Face detection

Show answer and explanation

Correct answer: A

Explanation

Image classification assigns an image to a predefined category. It is useful when the goal is to categorize entire images rather than detect specific objects within them.

Question 8 Single choice

What is the maximum image size that can be processed by using the prebuilt receipt model in Azure Al Document Intelligence?

  1. A

    5 MB

  2. B

    10MB

  3. C

    50 MB

  4. D

    100 MB

Show answer and explanation

Correct answer: D

Question 9 Single choice

A machine learning model produces a sentiment score ranging from -1 to 1 for each piece of text.

How should this value be interpreted?

  1. A

    It represents the probability of classification only

  2. B

    It represents sentiment ranging from negative to positive

  3. C

    It represents the number of entities in the text

  4. D

    It represents the number of tokens in the text

Show answer and explanation

Correct answer: B

Explanation

Sentiment analysis models often output scores where negative values indicate negative sentiment, positive values indicate positive sentiment, and values close to zero represent neutral sentiment.

Question 10 Single choice

During the process of Machine Learning, when should you review evaluation metrics.

  1. A

    Before you train a model.

  2. B

    After you clean the data.

  3. C

    Before you choose the type of model.

  4. D

    After you test a model on the validation data.

Show answer and explanation

Correct answer: D