[Mar 08, 2023] Dumps Collection AI-900 Test Engine Dumps Training With 165 Questions
Microsoft AI-900 Dumps - 100% Cover Real Exam Questions
Studying with Revision Books
Apart from the training endorsed by the certification vendor, it’s a good idea to exhaust the study guides from Amazon. Thus, here’s what you need to know about the two must-have revision books for the Microsoft AI-900 certification exam prep:
- Azure AI Fundamentals: Study Guide and Practice Exam for Microsoft AI-900 Exam
This guide by David Voss is based on the current test curriculum and targets trainees looking to gain foundational knowledge of the AI solutions and services on Azure. It comprises practice test questions and explanations that are consistent with Microsoft’s online learning tools. So, if you want to prepare with confidence and pass your exam easily, get your Kindle copy today from Amazon for only around $12.
- Microsoft AI MVP Book: Practical Guide to Microsoft AI Written by 17 AI and Azure MVPs from all around the world
This book is a collection of skills and knowledge from a team of AI and Azure specialists with countless years of experience in the field. It is written by Microsoft Azure MVPs including Leila Etaati, Anupama Natarajan, Ashraf Ghonaim, Cameron Vetter, David Rendon, etc. who used their cumulative years of experience to design a definitive study guide and a solution for all trainees preparing for AI-900 exam. The authors state it categorically that this book does not intend to cover everything there’s to know about Microsoft AI. And that it is important to combine it with other study materials to have a better chance of passing the Microsoft AI-900 exam. In a nutshell, this guide targets AI and ML developers, architects, data scientists, .net developers, and consultants who are already familiar with the foundational AI concepts. Remember it is not your best guide if you are starting from a zero, but an excellent choice if you are already familiar with most of the concepts in this field.
Exam Overview
This certification test is available in English, Spanish, Simplified Chinese, Japanese, French, Korean, and German. You will find different formats of questions while dealing with this Microsoft exam. These include multiple choice, drag and drop, build list, active screen, short answer, and best answer. The test costs $99 and the learners can register for it through Pearson VUE or Certiport.
How much AZ-900:Microsoft Azure AI Fundamentals Exam Cost
The exam cost of Microsoft Azure AI Fundamentals is 99 USD.
NEW QUESTION 11
In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad
- B. extracting key phrases from the audio recording of a meeting
- C. an Al character in a computer game that speaks audibly to a player
- D. generating live captions for a news broadcast
Answer: A,C
Explanation:
Explanation
Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Reference:
https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/
NEW QUESTION 12
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
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.
The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.
Box 2: No
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.
Box 3: Yes
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION 13
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:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml
NEW QUESTION 14
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 15
For a machine learning progress, how should you split data for training and evaluation?
- A. Use features for training and labels for evaluation.
- B. Randomly split the data into columns for training and columns for evaluation.
- C. Randomly split the data into rows for training and rows for evaluation.
- D. Use labels for training and features for evaluation.
Answer: C
Explanation:
Explanation
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data
NEW QUESTION 16
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
- A. translation
- B. key phrase extraction
- C. sentiment analysis
- D. entity recognition
Answer: D
Explanation:
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre- defined classes or types such as: person, location, event, product, and organization.
In this question, the square brackets indicate the entities such as DateTime, PersonType, Skill.
Reference:
https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity- linking?tabs=version-3-preview
NEW QUESTION 17
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: No
Box 2: Yes
Box 3: Yes
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
NEW QUESTION 18
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
Box 1: Image classification
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
Box 2: Object detection
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
Box 3: Semantic Segmentation
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION 19
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. language detection
- C. key phrase extraction
- D. entity recognition
Answer: C
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 20
When training a model, why should you randomly split the rows into separate subsets?
- A. to train the model twice to attain better accuracy
- B. to test the model by using data that was not used to train the model
- C. to train multiple models simultaneously to attain better performance
Answer: B
Explanation:
The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using "new" examples from the held-out datasets (validation and test datasets) to estimate the model's accuracy in classifying new data.
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,- A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%20data.
NEW QUESTION 21
You are developing a chatbot solution in Azure.
Which service should you use to determine a user's intent?
- A. Speech
- B. Translator Text
- C. QnA Maker
- D. Language Understanding (LUIS)
Answer: D
Explanation:
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 22
To complete the sentence, select the appropriate option in the answer area.
Computer vision capabilities can be Deployed to....................
Answer:
Explanation:
seetheanswerinbelowExplanation
Explanation:
Integrate a facial recognition feature into an app.
NEW QUESTION 23
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/
NEW QUESTION 24
Match the principles of responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
NEW QUESTION 25
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. fairness
- B. privacy and security
- C. transparency
- D. inclusiveness
Answer: C
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 26
What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Detect faces in an image.
- B. Train a custom image classification model.
- C. Translate the text in an image between languages.
- D. Recognize handwritten text.
Answer: A,D
Explanation:
Explanation
B: 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.
C: 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.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home Detect faces in an image - Face API Microsoft Azure provides multiple cognitive services that you can use to detect and analyze faces, including:
Computer Vision, which offers face detection and some basic face analysis, such as determining age.
Video Indexer, which you can use to detect and identify faces in a video.
Face, which offers pre-built algorithms that can detect, recognize, and analyze faces.
Recognize hand written text - Read API
The Read API is a better option for scanned documents that have a lot of text. The Read API also has the ability to automatically determine the proper recognition model
NEW QUESTION 27
You have a custom question answering solution.
You create a bot that uses the knowledge base to respond to customer requests. You need to identify what the bot can perform without adding additional skills. What should you identify?
- A. Register customer purchases.
- B. Provide customers with return materials authorization (RMA) numbers.
- C. Answer questions from multiple users simultaneously.
- D. Register customer complaints.
Answer: C
NEW QUESTION 28
To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________
Answer:
Explanation:
See the below in explanation:
Classification
NEW QUESTION 29
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:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0
NEW QUESTION 30
For a machine learning progress, how should you split data for training and evaluation?
- A. Use features for training and labels for evaluation.
- B. Randomly split the data into columns for training and columns for evaluation.
- C. Randomly split the data into rows for training and rows for evaluation.
- D. Use labels for training and features for evaluation.
Answer: C
Explanation:
Section: Describe Artificial Intelligence workloads and considerations
NEW QUESTION 31
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Classification
NEW QUESTION 32
......
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