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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 2: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 3: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 4: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
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NEW QUESTION # 315
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:
Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/
NEW QUESTION # 316
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:
NEW QUESTION # 317
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's Responsible AI Framework, the Reliability and Safety principle ensures that AI systems operate consistently, accurately, and as intended, even when confronted with unexpected data or edge cases. It emphasizes that AI systems must be tested, validated, and monitored to ensure stable performance and to prevent harm caused by inaccurate or unreliable outputs.
In the given scenario, the AI system is designed not to provide predictions when key fields contain unusual or missing values. This approach demonstrates that the system is built to avoid unreliable or unsafe outputs that could result from incomplete or corrupted data. Microsoft explicitly outlines that reliable AI systems must handle data anomalies and input validation properly to prevent incorrect predictions.
Here's how the other options differ:
* Inclusiveness ensures accessibility for all users, including those with disabilities or from different backgrounds. It's unrelated to prediction control or data reliability.
* Privacy and Security protects sensitive data and ensures proper handling of personal information, not system prediction logic.
* Transparency ensures that users understand how an AI system makes its decisions but doesn't address prediction reliability.
Thus, stopping a prediction when data is incomplete or abnormal directly supports the Reliability and Safety principle - it ensures that the AI model functions correctly under valid conditions and avoids unintended or harmful outcomes.
This principle aligns with Microsoft's Responsible AI guidance, which highlights that AI solutions must
"operate reliably and safely, even under unexpected conditions, to protect users and maintain trust."
NEW QUESTION # 318
You have the process shown in the following exhibit.
Which type AI solution is shown in the diagram?
Answer: A
NEW QUESTION # 319
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE; Each correct selection is worth one point.
Answer: B,D
Explanation:
According to the Microsoft Learn module "Describe features of Azure Machine Learning" and the AI-900 study guide, Azure Machine Learning designer supports extending workflows through custom code modules written in Python and R.
* Python is the most commonly used language for AI and machine learning due to its extensive library support (such as TensorFlow, Scikit-learn, and PyTorch).
* R is widely used for statistical computing and data visualization, making it valuable for analytical workloads.
In Azure Machine Learning, users can insert Python Script or Execute R Script modules within the visual designer to perform advanced operations or custom data transformations.
C# and Scala are not supported directly in Azure Machine Learning Designer. C# is more common in application development, and Scala is primarily used in big data frameworks like Apache Spark.
Hence, the correct answers are C. Python and D. R.
NEW QUESTION # 320
......
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