Explanation:
The percentage of false positives is [0].
The value for the number of true positives divided by the total number of true positives and false negatives is [100].
The screenshot shows the Performance tab of a Custom Vision object detection model after training.
Two key performance metrics are in focus:
False Positives (FP):
False positives occur when the model incorrectly predicts an object that isn't present.
In the provided exhibit, the model shows 0% false positives.
This means it did not incorrectly classify any non-object as an object.
Recall (True Positives ÷ (True Positives + False Negatives)):
This measures how many of the actual objects were correctly detected.
Formula: Recall = TP / (TP + FN).
In the performance report, recall is 100%, meaning the model correctly detected all the actual objects (no false negatives).
Evaluating the answer choices:
False positives: 0%
Recall (TP ÷ (TP + FN)): 100%
Correct Answers:
False positives # 0
Recall # 100
Microsoft References
Custom Vision performance metrics
Precision, recall, and accuracy in machine learning

Explanation:

In relational database systems such as Microsoft SQL Server or Azure SQL Database, the following definitions apply:
Stored Procedure:
A stored procedure is a precompiled block of Transact-SQL (T-SQL) code stored in the database. It can contain control-of-flow statements, queries, and business logic. When executed, it runs as a single unit within the database engine. This matches the definition of " a block of code that runs in a database. " Table:
A table is a structured set of rows and columns used to store data, not a block of executable code.
View:
A view is a virtual table based on the result of a SQL query. It is used for abstraction and simplification of queries, not for executing code blocks.
Index:
An index is a database structure that improves the speed of data retrieval. It is not executable code.
Thus, the only option that correctly describes " a block of code that runs in a database " is a stored procedure.
The answer: A stored procedure
Microsoft References
Stored Procedures (Database Engine)
Azure SQL Database - Stored procedures overview
SQL Server Views
SQL Server Tables
SQL Server Indexes

Explanation:

Comprehensive Detailed Explanation
The scenario:
You are using Azure Content Moderator to filter chatbot messages.
You must detect sexually explicit language.
The question asks: which section of the response provides the category score, and which category number corresponds to sexual content.
Step 1 - Understand Content Moderator response structure
Content Moderator returns several sections:
Classification # Provides scores for categories of potentially unsafe or inappropriate content.
PII # Identifies personally identifiable information (phone numbers, email, addresses, etc.).
Terms # Identifies terms from a custom or built-in blocklist.
Since the requirement is sexually explicit language detection, the correct section is Classification.
Step 2 - Identify the correct Category
Content Moderator's Classification section returns three default categories:
Category 1: Potentially sexually explicit or adult content.
Category 2: Potentially sexually suggestive content.
Category 3: Offensive or derogatory language.
Since the question specifies sexually explicit language, the correct category is 1.
Correct Answer Mapping:
Section: Classification
Category: 1
Microsoft References
Content Moderator - Text moderation (classification categories explained) Content Moderator overview and response schema

Explanation:
Generate alt text
To automatically suggest descriptive captions for images (usable as alt text), use the Computer Vision Describe Image API. It returns natural-language captions summarizing the image content. Endpoint: /vision
/v3.2/describe . Setting maxCandidates=1 gives a single best caption.
Detect inappropriate content
To block adult/racy/gory images with minimal effort, use Computer Vision Analyze Image with the Adult visual feature. This returns flags ( isAdult , isRacy , isGory ) and confidence scores. The provided option includes Adult,Description which still returns the needed adult/racy/gore signals, satisfying the requirement.
These choices minimize development effort by using built-in Computer Vision capabilities without training a custom model or wiring up multiple services.
References
Microsoft Learn - Describe images (Computer Vision): "The Describe Image API returns a list of natural language sentences that describe the image."
https://learn.microsoft.com/azure/ai-services/computer-vision/overview-image-analysis#describe-images Microsoft Learn - Analyze images (Adult/Racy/Gore detection): "Use the Adult feature to detect adult, racy, and gory content."
https://learn.microsoft.com/azure/ai-services/computer-vision/overview-image-analysis#adult-racy-and-gory- content-detection

Explanation:

Box 1: QnA Maker Editor
Scenario: Provide all employees with the ability to edit Q&As.
The QnA Maker Editor (read/write) has the following permissions:
Create KB API
Update KB API
Replace KB API
Replace Alterations
"Train API" [in
new service model v5]
Box 2: Contributor
Scenario: Only senior managers must be able to publish updates.
Contributor permission: All except ability to add new members to roles
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/reference-role-based-access-control