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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Available Languages: | English, German, French, Indonesian (Indonesia), Korean, Chinese (Simplified), Japanese, Spanish, Portuguese (Brazil), Russian, Chinese (Traditional), Italian, Arabic (Saudi Arabia) |
| Exam Format: | Scenario-based questions, Multiple choice, Multiple select |
| Certificate Validity Period: | Valid indefinitely |
| Related Certifications: | Microsoft Certified: Azure Fundamentals Microsoft Certified: Azure Data Fundamentals |
| Exam Price: | $99 USD |
| Real Exam Qty: | 40–60 |
| Passing Score: | 700 (on a scale of 1–1000) |
| Exam Duration: | 45 minutes |
| Recommended Training: | Microsoft Learn: Azure AI Fundamentals Learning Path Instructor-led Training: AI-900 Course |
| Exam Registration: | Microsoft Certification Registration Pearson VUE Registration |
| Sample Questions: | Microsoft AI-900 Sample Questions |
| Exam Way: | Online proctored or onsite testing at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/ |
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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 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.
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.
The AI-900 exam is an entry-level certification that does not require any prerequisites. However, it is recommended that you have some basic knowledge of cloud computing and programming concepts before taking AI-900 exam. AI-900 exam is available in multiple languages and can be taken online or at a testing center.
Microsoft AI-900: Microsoft Azure AI Fundamentals exam is a certification that is designed to validate the candidate's knowledge and understanding of Artificial Intelligence (AI) and its applications on the Azure cloud platform. AI-900 exam is intended for individuals who are interested in understanding the basic concepts of AI and how it can be applied in various scenarios.
NEW QUESTION # 242
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
During model training, a portion of the dataset (commonly 70-80%) is used to teach the machine learning algorithm to identify patterns and relationships between input features and the output label. The remaining data (usually 20-30%) is held back to evaluate the model's performance and verify its accuracy on unseen data. This ensures the model is not overfitted (too tightly fitted to training data) and can generalize well to new inputs.
Key steps highlighted in Microsoft Learn materials:
* Model Training: Use the training data to fit the model - the algorithm learns relationships between input features and labels.
* Model Evaluation: Use the test or validation data to assess the accuracy, precision, recall, or other metrics of the trained model.
* Model Deployment: Once validated, the model is deployed to make real-world predictions.
Other options explained:
* Feature engineering: Involves preparing and transforming input data, not splitting datasets for training and testing.
* Time constraints: Not a machine learning process step.
* Feature stripping: Not a recognized ML concept.
* MLflow models: Refers to an open-source tool for tracking and managing models, not dataset splitting or training.
Thus, when you use a portion of the dataset to prepare and train a machine learning model, and retain the rest to verify results, the process is known as model training.
NEW QUESTION # 243
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:
Statements
Yes
No
A bot that responds to queries by internal users is an example of a conversational AI workload.
# Yes
An application that displays images relating to an entered search term is an example of a conversational AI workload.
# No
A web form used to submit a request to reset a password is an example of a conversational AI workload.
# No
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, conversational AI workloads are those that enable interaction between humans and AI systems through natural language conversation, either by text or speech. These workloads are typically implemented using Azure Bot Service, Azure Cognitive Services for Language, and Azure OpenAI Service. The key characteristic of a conversational AI workload is the presence of dialogue-the AI interprets user intent and provides a meaningful, contextual response in a conversation-like manner.
* "A bot that responds to queries by internal users is an example of a conversational AI workload." # YESThis fits the definition perfectly. A chatbot that helps employees (internal users) by answering questions about policies, IT issues, or HR procedures is a typical example of conversational AI. It uses natural language understanding to interpret questions and provide automated responses. Microsoft Learn explicitly identifies chatbots as conversational AI solutions designed for both internal and external interactions.
* "An application that displays images relating to an entered search term is an example of a conversational AI workload." # NOThis is not conversational AI because there is no dialogue or language understanding involved. It is an example of information retrieval or computer vision if it uses image recognition, but not conversation.
* "A web form used to submit a request to reset a password is an example of a conversational AI workload." # NOA password reset form is a simple UI-driven process that doesn't require AI or conversational logic. It performs a fixed function based on user input but does not understand or respond to natural language.
Therefore, based on the AI-900 study guide, only the first statement is an example of a conversational AI workload, while the second and third statements are not.
NEW QUESTION # 244
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:
NEW QUESTION # 245
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data
NEW QUESTION # 246
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: verification
Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.
Box 2: similarity
Box 3: Grouping
Box 4: identification
Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
NEW QUESTION # 247
......
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