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:
API Endpoint Selection
The constraint states that the content of the text must remain within the Americas Azure geography .
" api.cognitive.microsofttranslator.com " : This is the global endpoint , which doesn ' t guarantee data residency in a specific geography.
" api-apc.cognitive.microsofttranslator.com " : This endpoint refers to the Asia Pacific geography .
" api-nam.cognitive.microsofttranslator.com " : This endpoint refers to the North and South America (NAM) geography .
Therefore, to meet the data residency requirement for the Americas, you must select " api-nam.cognitive.
microsofttranslator.com " .
2. Path/Query String Selection
The requirement is to translate the text to a single language . The input text can be in " one of many languages, " meaning the source language isn ' t fixed and should be detected automatically. The language to translate to is implied to be English ( en ) based on the options available and common translation scenarios.
" /translate?from=en " : This is incorrect because it specifies the source language as English, which contradicts the " text can be in one of many languages " requirement (implying automatic source language detection).
" /translate?suggestedFrom=en " : This is not the standard way to initiate a translation request and seems to suggest a source language, which is unnecessary if auto-detection is desired/possible.
" /translate?to=en " : This is the correct and standard way to request a translation where the source language is automatically detected , and the target language is English ( en ) . This fulfills the translation requirement.
" /detect?to=en " / " /detect?from=en " : These endpoints are used for language detection and not for the translation itself. The prompt explicitly asks to develop code to translate the text.

Explanation:

To identify city names with Azure AI Language, you use the Text Analytics client and its Named Entity Recognition (NER) capability. In the .NET SDK, create a TextAnalyticsClient and call RecognizeEntities(...).
This returns a CategorizedEntityCollection, where each CategorizedEntity has properties such as Text, Category, SubCategory, and ConfidenceScore.
City mentions are recognized under the broader Location category, and the more specific type is surfaced in SubCategory (commonly "City"). Therefore, iterating the returned entities and filtering with entity.
SubCategory.Contains("City") correctly locates city names. Using DocumentAnalysisClient or FormRecognizerClient would be incorrect because those are for document/form extraction scenarios, not general-language NER. QuestionAnsweringClient is for Q&A over knowledge sources. Hence the correct client is TextAnalyticsClient, and the correct discriminator is SubCategory.
Microsoft Azure AI References (titles only)
* Azure AI Language - Text Analytics client library: Recognize Entities
* Azure AI Language - Named Entity Recognition concepts and categories
* Azure AI Language - CategorizedEntity model (Category, SubCategory, ConfidenceScore)

* In Power BI, reports are interactive, visual, and support KPIs, drill-through, and drill-down functionality.
* Power Apps is for app development, not analytics.
* A view is a database object, not a visualization solution.
* Dataflow is for ETL/ELT (data preparation), not reporting.
The answer: A
Reference: Power BI Reports