Microsoft Certified: Azure AI Fundamentals AI-900 Dumps | Updated Oct 23, 2021 - PassTestking
Master 2021 Latest The Questions Microsoft Certified: Azure AI Fundamentals and Pass AI-900 Real Exam!
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Understanding of functional and technical aspects of AI workloads and considerations (15-20%)
The following will be discussed in this section:
- Describe considerations for reliability and safety in an AI solution that assess the percentage of safe up time in a model that is being scaled for an enhanced number of users
- identify natural language processing or knowledge mining workloads that helps deep learning from a vast array of data resources to work on the problem at hand
- Describe considerations for fairness in an AI solution which helps in assessing fairness percentage of a machine learning model in a sandbox
- Identify prediction/forecasting workloads techniques which allow predicting the amount/number of business products or services to be purchased during a defined future period through analysis of past history
- Describe considerations for privacy and security in an AI solution that provides grounds for zero trust, identity theft protection, fraud protection and prevention as well as information containment
- Identify features of anomaly detection workloads which will help in detecting bugs and lapses in the product or service by using time-series data to automatically detect anomalies in your apps
- Identify computer vision workloads by extracting digital animations and mathematical valuables from data
- Describe considerations for accountability in an AI solution that peg AI models to being responsible for the reasons and domains they operate in
- Describe considerations for inclusiveness in an AI solution to uphold ethical and moral code in information processing and built models
- Identify conversational AI workloads by building, managing and using intelligent AI powered bots
- Describe considerations for transparency in an AI solution that allows users to know the way their data, identity and credentials are being used in a digital AI model
NEW QUESTION 15
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. sentiment analysis
- B. key phrase extraction
- C. entity recognition
- D. language detection
Answer: B
Explanation:
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in 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
NEW QUESTION 16
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
- A. classification
- B. regression
- C. clustering
Answer: B
NEW QUESTION 17
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 18
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes
Azure bot service engages with customers in a conversational manner.
Box 3: No
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
NEW QUESTION 19
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION 20
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
- A. classification
- B. clustering
- C. regression
Answer: A
NEW QUESTION 21
In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. creating an automated public address system for a train station
- B. providing closed captions for recorded or live videos
- C. an in-car system that reads text messages aloud
- D. creating a transcript of a telephone call or meeting
Answer: B,D
Explanation:
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation/Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features
NEW QUESTION 22
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-in/blog/microsoft-conversational-ai-tools-enable-developers-to-build-connect-and-manage-intelligent-bots
NEW QUESTION 23
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION 24
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. image classification
- B. semantic segmentation
- C. optical character recognition (OCR)
- D. object detection
Answer: D
Explanation:
Section: Describe features of computer vision workloads on Azure
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 25
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 Max concurrent iterations to 0.
- B. Enable Explain best model.
- C. Set Primary metric to accuracy.
- D. Set Validation type to Auto.
Answer: B
Explanation:
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation:
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 explain ability, 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- learning-service/
NEW QUESTION 26
What is a use case for classification?
- A. analyzing the contents of images and grouping images that have similar colors
- B. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
- C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
- D. predicting how many minutes it will take someone to run a race based on past race times
Answer: A
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
NEW QUESTION 27
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
Answer: B
Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
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 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
NEW QUESTION 28
You are developing a chatbot solution in Azure.
Which service should you use to determine a user's intent?
- A. Language Understanding (LUIS)
- B. Translator Text
- C. Speech
- D. QnA Maker
Answer: A
Explanation:
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
NEW QUESTION 29
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.
Answer:
Explanation:
Explanation
Box 1: 11
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
NEW QUESTION 30
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