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Most people are nervous and anxious to take part in the AI-900 exam for the first time. Then it is easy for them to make mistakes. So it is important to get familiar with the real test environment. Also, the real test environment of the AI-900 Study Materials can help you control time. After all, you must submit your practice in limited time in AI-900 practice materials. Trust in our AI-900 training guide, and you will get success for sure.
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:
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.
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.
NEW QUESTION # 56
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?
Answer: A
Explanation:
Section: Describe Artificial Intelligence workloads and considerations
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 # 57
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:
The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.
NEW QUESTION # 58
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", regression is a supervised machine learning technique used to predict continuous numerical values based on one or more input features. In this scenario, the task is to predict a vehicle's miles per gallon (MPG)-a continuous numeric value-based on several measurable factors such as weight, engine power, and other specifications.
Regression models learn the mathematical relationship between input variables (independent features) and a numeric target variable (dependent outcome). Common regression algorithms include linear regression, decision tree regression, and support vector regression. In the example, the model would analyze historical data of vehicles and learn patterns that map characteristics (like engine size, horsepower, and weight) to fuel efficiency. Once trained, it can predict the MPG for a new vehicle configuration.
The other options describe different problem types:
* Classification predicts discrete categories (for example, whether a car is "fuel efficient" or "not fuel efficient"), not continuous values.
* Clustering is an unsupervised learning method that groups data points based on similarities without predefined labels, not predictive modeling.
* Anomaly detection identifies data points that significantly deviate from normal patterns, such as detecting engine sensor failures or fraudulent transactions.
Since predicting MPG involves estimating a numeric value within a continuous range, regression is the most appropriate model type.
In summary, per AI-900 training content, regression models are used when the output variable is numeric, classification for categorical outputs, and clustering for pattern discovery. Therefore, predicting miles per gallon based on vehicle features is a textbook example of a regression problem in Azure Machine Learning.
NEW QUESTION # 59
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
Text Description automatically generated
NEW QUESTION # 60
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier
NEW QUESTION # 61
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