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Microsoft Fundamentals AI-900 Practice Test Questions Answers
QUESTION 1
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Predict stock prices.
B. Detect brands in an image.
C. Detect the color scheme in an image
D. Translate text between languages.
E. Extract key phrases.
Correct Answer: BE
B: Azure\\’s Computer Vision service gives you access to advanced algorithms that process images and return
information based on the visual features you\\’re interested in. For example, Computer Vision can determine whether an
image contains adult content, find specific brands or objects, or find human faces.
E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to
extract printed and handwritten text from images and documents. It uses the latest models and works with text on a
variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two
OCR APIs support extracting printed text in several languages.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
QUESTION 2
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Correct Answer:
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.
Incorrect Answers:
Computer Vision:
Azure\\’s Computer Vision service provides developers with access to advanced algorithms that process images and
return information based on the visual features you\\’re interested in. For example, Computer Vision can determine
whether an
image contains adult content, find specific brands or objects, or find human faces.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home
QUESTION 3
You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure
that the model meets the Microsoft transparency principle for responsible AI. What should you do?
A. Set Validation type to Auto.
B. Enable Explain the best model.
C. Set Primary metric to accuracy.
D. Set Max concurrent iterations to 0.
Correct Answer: B Model Explain Ability.
Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily
regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key
aspect
of this is understanding the relationship between input variables (features) and model output. Knowing both the
magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better
understand and
explain the model. With model explainability, we enable you to understand feature importance as part of automated ML
runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learningservice/
QUESTION 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:
Correct Answer:
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
QUESTION 5
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Correct Answer:
In machine learning, if you have labeled data, that means your data is marked up or annotated, to show the target,
which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription,
or processing.
Incorrect Answers:
Not features: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful
features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable
inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://www.cloudfactory.com/data-labeling-guide
QUESTION 6
Which metric can you use to evaluate a classification model?
A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE)
Correct Answer: A
What does a good model look like?
A ROC curve that approaches the top left corner with a 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
QUESTION 7
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. a telephone answering service that has a pre-recorder message
B. a chatbot that provides users with the ability to find answers on a website by themselves
C. telephone voice menus to reduce the load on human resources
D. a service that creates frequently asked questions (FAQ) documents by crawling public websites
Correct Answer: BC
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to
communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all
businesses, self-service is a critical facet of any customer-facing communications strategy.
Incorrect Answers:
D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward
algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web
content.
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
QUESTION 8
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:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and
includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language
detection.
Box 1: Yes You can detect which language the input text is written in and report a single language code for every
document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural
languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations,
date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more
information on the web.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
QUESTION 9
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
A. entity recognition
B. keyphrase extraction
C. sentiment analysis
D. language detection
Correct Answer: B
Broad entity extraction: Identify important concepts in a text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in a text, including key phrases and named
entities such as people, places, and organizations.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
QUESTION 10
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
Correct Answer:
With Microsoft\\’s Conversational AI tools developers can build, connect, deploy, and manage intelligent bots that
naturally interact with their users on a website, app, Cortana, Microsoft Teams, Skype, Facebook Messenger, Slack,
and more.
Reference: https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connectand-manage-intelligent-bots
QUESTION 11
Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
A. anomaly detection
B. semantic segmentation
C. regression
D. natural language processing
Correct Answer: D
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection,
keyphrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative, or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
QUESTION 12
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
A. classification
B. regression
C. clustering
Correct Answer: B
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression
problem. This graph can be used to measure the performance of a model as the closer to the y=x line the predicted values
are, the
better the accuracy of a predictive model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
QUESTION 13
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit. 
Which type of computer vision was used?
A. object detection
B. semantic segmentation
C. optical character recognition (OCR)
D. image classification
Correct Answer: A
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
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