Exercise : Exam AI 900 Microsoft Azure AI Fundamentals MCQ Questions and Answers
1.
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. |
| B. |
| C. |
| D. |
Correct Answer : D. transparency Most Voted
Description :
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.
Incorrect Answers:
B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed.
Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.
D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy.
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
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2.
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
| A. |
| B. |
| C. |
| D. |
Correct Answer : C. Custom Vision
Description :
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview
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3. Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
| A. |
| B. |
| C. |
| D. |
Correct Answer : B. Form Recognizer
Description :
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer
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4.
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.
Hot Area:
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. Yes, No, Yes, No
Description :
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.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
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5. Which analytical task of the computer vision service returns bounding box coordinates?
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. object detection
Description :
Detecting objects identifies common objects and, for each, returns bounding box coordinates. Image categorization assigns a category to an image, but it does not return bounding box coordinates. Tagging involves associating an image with metadata that summarizes the attributes of the image, but it does not return bounding box coordinates. OCR detects printed and handwritten text in images, but it does not return bounding box coordinates.
References :
Get started with image analysis on Azure - Training | Microsoft Learn
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6.
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. inclusiveness
Description :
Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
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7.
HOTSPOT -
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Hot Area:
| A. |
| B. |
| C. |
| D. |
Correct Answer : C. 11 1,033
Description :
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033 -
FN = False Negative -
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
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8.
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
| A. |
| B. |
| C. |
Correct Answer : A. regression Most Voted
Description :
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
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9.
HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
| A. |
| B. |
| C. |
| D. |
Correct Answer : D. fairness
Description :
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
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10.
DRAG DROP -
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.
Select and Place:
| A. |
| B. |
| C. |
| D. |
Correct Answer : B. Conversational AI, Computer Vision, Natural language processing
Description :
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
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