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In the last section, you will face with the following subtopics:
NEW QUESTION # 291
You have a dataset that contains the columns shown in the following table.
You have a machine learning model that predicts the value of ColumnE based on the other numeric columns.
Which type of model is this?
Answer: B
NEW QUESTION # 292
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 293
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:
The correct matches are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." These materials explain that facial recognition tasks can be categorized into four major operations: verification, identification, similarity, and grouping. Each task serves a distinct purpose in facial recognition scenarios.
* Verification - "Do two images of a face belong to the same person?"The verification task determines whether two facial images represent the same individual. Azure Face API compares the facial features and returns a confidence score indicating the likelihood that the two faces belong to the same person.
* Similarity - "Does this person look like other people?"The similarity task compares a face against a collection of faces to find visually similar individuals. It does not confirm identity but measures how closely two or more faces resemble each other.
* Grouping - "Do all the faces belong together?"Grouping organizes a set of unknown faces into clusters based on similar facial features. This is used when identities are not known beforehand, helping discover potential duplicates or visually similar clusters within an image dataset.
* Identification - "Who is this person in this group of people?"The identification task is used when the system tries to determine who a specific person is by comparing their face against a known collection (face database or gallery). It returns the identity that best matches the input face.
According to Microsoft's AI-900 training, these tasks form the basis of Azure Face API's capabilities. Each helps solve a different type of facial recognition problem-from matching pairs to discovering unknown identities-making them essential components of responsible AI-based vision systems.
NEW QUESTION # 294
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 295
What should you implement to identify hateful responses returned by a generative Al solution?
Answer: A
Explanation:
When using generative AI models such as those in Azure OpenAI, it's critical to monitor and control the model's outputs to prevent the generation of harmful, hateful, or unsafe content. According to Microsoft's Responsible AI guidelines and Azure OpenAI safety systems, this is achieved through content filtering mechanisms.
Content filtering automatically analyzes generated text and blocks or flags responses containing hate speech, harassment, sexual content, or violent language. Azure OpenAI applies these filters by default through its built-in content moderation system, which categorizes responses into safety levels (safe, review, block).
Option review:
* A. Prompt engineering: Helps guide responses but does not detect hate content automatically.
* B. Abuse monitoring: Refers to manual or post-processing review, not real-time automated filtering.
* C. Content filtering: # Correct - actively detects and prevents hateful or unsafe responses.
* D. Fine-tuning: Adjusts model behavior using additional training data but doesn't ensure safety monitoring.
NEW QUESTION # 296
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