6.1.2. Translation Services

Translator converts text between languages. Supports document translation and custom terminology.

💡 First Principle: Unstructured text hides structured information—customer reviews contain sentiment, contracts contain entity names, articles contain key topics. Text Analysis extracts this structure, but different extractions answer different questions: "How do customers feel?" → sentiment. "What people/places/orgs are mentioned?" → NER. "What's this about?" → key phrases. The exam tests whether you can match the business question to the correct analysis type.

🔧 Implementation Reference: Azure AI Language

The following methods are the most commonly tested. Each takes text input and returns structured output.

Key Methods:

Testable Pattern:

from azure.ai.textanalytics import TextAnalyticsClient

client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
result = client.analyze_sentiment(["The hotel was great but the food was terrible."], show_opinion_mining=True)[0]
print(result.sentiment)  # "mixed"

CLI Equivalent (REST):

curl -X POST "https://{endpoint}/language/:analyze-text?api-version=2023-04-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{"kind": "SentimentAnalysis", "analysisInput": {"documents": [{"id": "1", "text": "The hotel was great"}]}}'

Azure AI Language Documentation

Azure Translator:

⚠️ Exam Trap: Translator requires both key AND region headers.

BLEU scores measure translation quality. Know these ranges for exam questions about translation model evaluation.

BLEU Score (Translation Quality):

Azure Translator Documentation