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AI-102 Practice Question: Implement natural language processing solutions

Exhibit

Refer to the exhibit.

$endpoint = "https://mytextanalytics.cognitiveservices.azure.com/"
$key = "myKey"
$headers = @{
    "Ocp-Apim-Subscription-Key" = $key
    "Content-Type" = "application/json"
}
$body = @{
    "documents" = @(
        @{
            "id" = "1"
            "text" = "The product is excellent and very useful."
            "language" = "en"
        }
    )
} | ConvertTo-Json
$uri = "$endpoint/text/analytics/v3.1/sentiment"
$response = Invoke-RestMethod -Uri $uri -Method Post -Headers $headers -Body $body

You run the PowerShell script above to call the Text Analytics API. The response shows a sentiment label of 'positive' with a score of 0.99. However, you expected 'negative' because the word 'excellent' was meant to be sarcastic. What is the most likely reason for this result?

⚠ Common exam trap

A common mix-up: candidates assume the API can infer sarcasm or implied sentiment, when in fact the model performs only literal, surface-level sentiment analysis based on word choice and phrase patterns.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The model does not detect sarcasm and interprets the text literally.

The Text Analytics API performs sentiment analysis using a machine learning model that evaluates the literal wording of the text. It does not have built-in capability to detect sarcasm, irony, or implied meaning. Therefore, the word 'excellent' is interpreted as positive regardless of the intended sarcastic tone, resulting in a high positive score.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The score threshold for negative sentiment is too high.
Why it's wrong here
The score is near 1.0, strongly positive.
✗
The language parameter is set incorrectly.
Why it's wrong here
Language is correctly set to English.
✗
The API version does not support sentiment analysis.
Why it's wrong here
Version 3.1 supports sentiment analysis.
✓
The model does not detect sarcasm and interprets the text literally.
Why this is correct
Sarcasm detection is not a built-in feature.

Go deeper

AI-102 Practice Question: Implement natural language processing solutions

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question