Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) - AI-102 Korean Exam Practice Test

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

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Explanation:

To make the chatbot respond only when the answer's confidence is # 95%, you must set the request body property confidenceScoreThreshold to 0.95 in the Query knowledge bases operation of Custom question answering. The REST API for querying a Custom question answering project is:
POST { Endpoint }/language/ :query-knowledgebases?projectName= {projectName} & deploymentName=
{deploymentName} & api-version= 2021 - 10 - 01
Here, projectName identifies the Custom question answering project you're querying (the screenshot's placeholder < chatbot-project > ), while deploymentName specifies which deployment (for example, production ) to use. The request body includes confidenceScoreThreshold (0-1) to filter out answers below that score; setting it to 0.95 enforces the requirement.
Example snippet:
curl -X POST " $LANGUAGE_ENDPOINT /language/:query-knowledgebases?projectName= < chatbot- project > & deploymentName=production & api-version=2021-10-01 " \
-H " Ocp-Apim-Subscription-Key: $LANGUAGE_KEY " \
-H " Content-Type: application/json " \
-d ' {
" question " : " How much energy does my phone have left? " ,
" confidenceScoreThreshold " : 0.95
} '
References
REST API: Question Answering - Get Answers (shows endpoint with projectName / deploymentName and the confidenceScoreThreshold request property). Microsoft Learn REST API (newer version) parameter list for projectName / deploymentName . Microsoft Learn

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Explanation:

Explanation:

First blank: speech_recognition_language
Second blank: add_target_language
speech_translation_config = speechsdk.translation.SpeechTranslationConfig( subscription=os.environ.get( ' SPEECH_KEY ' ), region=os.environ.get( ' SPEECH_REGION ' ) )
# Source (recognition) language: English (US)
speech_translation_config.speech_recognition_language = " en-US "
# Target translation language: Italian
speech_translation_config.add_target_language( " it " )
audio_config = speechsdk.audio.AudioConfig(use_default_microphone= True ) recognizer = speechsdk.translation.TranslationRecognizer( translation_config=speech_translation_config, audio_config=audio_config ) Comprehensive Detailed Explanation along with All References available from Microsoft Azure AI Solution at the end of the explanation To translate speech, configure a SpeechTranslationConfig with (1) the recognition language of the input audio and (2) one or more target languages for translation. In Python, you set the input language with the speech_recognition_language property (e.g., " en-US " ), and you add a target with add_target_language( " < locale > " ) (e.g., " it " for Italian). This exactly matches the SDK guidance and examples for speech translation. Microsoft Learn speech_recognition_language specifies the locale used by the recognizer to understand the spoken input.
add_target_language( " it " ) adds Italian as a translation output language.
Microsoft References
How to translate speech with the Speech SDK (Python examples show translation_config.
speech_recognition_language = from_language and translation_config.add_target_language(...) ). Microsoft Learn SpeechTranslationConfig (Python) reference - add_target_language(language: str) . Microsoft Lea rn Recognize speech (property usage for speech_recognition_language ). Microsoft Learn Language support for Speech service (locales and codes for recognition/translation)

Explanation:
Stream processing has access to the most recent data received or data within a rolling time window. # Yes Batch processing must occur immediately and have latency in the order of seconds or milliseconds. # No Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages.
# Yes
Comprehensive Detailed Explanation
Stream processing has access to the most recent data received or data within a rolling time window.
Correct.
Stream processing handles real-time or near real-time data.
It operates on a continuous data stream and often uses a rolling time window for analytics.
The answer: Yes
Batch processing must occur immediately and have latency in the order of seconds or milliseconds.
Incorrect.
Batch processing is designed for large volumes of data collected over time.
It typically has high latency (minutes, hours, or even days), not immediate execution.
The answer: No
Stream processing is used for simple response functions, aggregates, or calculations such as rolling averages.
Correct.
Common stream analytics tasks include event-driven responses, real-time aggregations, anomaly detection, and rolling averages.
The answer: Yes
Correct Answers:
Yes
No
Yes
Microsoft References
Batch vs. Stream processing in Azure
Azure Stream Analytics overview

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Explanation:

Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance.
Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity

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