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| Section | Weight | Objectives |
|---|---|---|
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision - Identify common computer vision tasks |
| Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
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NEW QUESTION # 273
brectly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 274
What is a use case for classification?
Answer: B
Explanation:
Section: Describe features of computer vision workloads on Azure
Explanation:
Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
Incorrect Answers:
A: This is Regression.
B: This is Clustering.
D: This is Regression.
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 # 275
When training a model, why should you randomly split the rows into separate subsets?
Answer: A
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%
NEW QUESTION # 276
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 # 277
You are building an Al-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible Al principle?
Answer: A
Explanation:
According to Microsoft's Responsible AI Principles, transparency refers to ensuring that AI systems are understandable and that their decisions or predictions can be explained clearly to users. When building an AI- based loan approval app, documenting the reasons for approving or rejecting a loan and making this information available to the applicant ensures that the system's decision-making process is transparent and easily interpretable.
Transparency in AI involves making the model's inputs, features, and reasoning process visible and comprehensible to both developers and users. For a loan approval application, this could mean showing which factors-such as income, credit score, or debt ratio-influenced the outcome. Microsoft emphasizes that users should be aware when AI is making decisions that affect them and should have access to an explanation of how those decisions were made.
* Fairness (A) ensures that AI systems treat all individuals equitably without bias.
* Inclusiveness (B) focuses on accessibility and ensuring AI benefits all groups.
* Accountability (D) ensures that humans remain responsible for the outcomes of AI systems.
While accountability supports ethical oversight, the specific act of explaining and documenting the AI's decision process aligns directly with transparency.
Therefore, the verified answer is C. Transparency.
NEW QUESTION # 278
......
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