8.4. Implementation Quick Reference

This section provides condensed implementation details for code completion and drag-and-drop questions. Memorize the Required columns.

SDK Packages and Classes

Required Headers by Service

Required vs. Optional Parameters

Azure OpenAI Chat Completions:

DALL-E Image Generation:

Content Safety:

Code Completion Patterns

Pattern 1: Authentication Setup

# Fill in the blank: _______________
from azure.identity import DefaultAzureCredential  # For managed identity
from azure.core.credentials import AzureKeyCredential  # For API key

Pattern 2: Azure OpenAI Client

from openai import AzureOpenAI
client = AzureOpenAI(
    azure_endpoint=endpoint,  # Required
    api_key=key,              # Required (or use Azure AD)
    api_version="2024-08-01-preview"  # Required
)

Pattern 3: Chat Completion

response = client.chat.completions.create(
    model="gpt-4o",           # Required: deployment name
    messages=[                # Required: message array
        {"role": "system", "content": "You are helpful."},
        {"role": "user", "content": "Hello"}
    ],
    temperature=0.7           # Optional
)
answer = response.choices[0].message.content  # Access response

Pattern 4: Image Analysis

from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.ai.vision.imageanalysis.models import VisualFeatures

client = ImageAnalysisClient(endpoint=endpoint, credential=credential)
result = client.analyze(
    image_data=image_bytes,   # Or image_url for URL
    visual_features=[VisualFeatures.CAPTION, VisualFeatures.READ]
)

Pattern 5: Document Intelligence

from azure.ai.documentintelligence import DocumentIntelligenceClient

client = DocumentIntelligenceClient(endpoint=endpoint, credential=credential)
poller = client.begin_analyze_document(
    "prebuilt-invoice",       # Model ID
    analyze_request=file_stream
)
result = poller.result()

Pattern 6: Speech-to-Text

import azure.cognitiveservices.speech as speechsdk

speech_config = speechsdk.SpeechConfig(subscription=key, region=region)
recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config)
result = recognizer.recognize_once()
text = result.text

Pattern 7: Text-to-Speech with SSML

speech_config.speech_synthesis_voice_name = "en-US-JennyNeural"
synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)
result = synthesizer.speak_ssml_async(ssml_string).get()

Pattern 8: Azure AI Search

from azure.search.documents import SearchClient
from azure.search.documents.models import VectorizedQuery

search_client = SearchClient(endpoint, index_name, credential)
results = search_client.search(
    search_text="query",
    vector_queries=[VectorizedQuery(vector=embedding, k_nearest_neighbors=50, fields="contentVector")]
)

Container Deployment Quick Reference

docker run --rm -it -p 5000:5000 \
  mcr.microsoft.com/azure-cognitive-services/{service}:{tag} \
  ApiKey={KEY} \
  Billing={ENDPOINT} \  # Still required!
  Eula=accept

SSML Quick Reference

<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" 
       xmlns:mstts="https://www.w3.org/2001/mstts" xml:lang="en-US">
    <voice name="en-US-JennyNeural">
        <prosody rate="slow" pitch="+5%">Slower, higher</prosody>
        <break time="500ms"/>
        <say-as interpret-as="date">2024-01-15</say-as>
        <mstts:express-as style="cheerful">Happy tone!</mstts:express-as>
    </voice>
</speak>

Decision Trees Quick Reference

Service Selection:

• Image input → Computer Vision / Custom Vision
• Audio input → Speech Services
• Text input → Language Services
• Document input → Document Intelligence
• Generate content → Azure OpenAI

Authentication:

• Development → API Key (AzureKeyCredential)
• Production → Managed Identity (DefaultAzureCredential)

Network:

• Development → Public endpoint
• Production (Azure only) → Private endpoint

Service Limits Quick Reference

Microsoft frequently tests knowledge of service limits and thresholds. Memorize these key values.

Input Size Limits:

Training Minimums:

Quality Thresholds:

Rate Limits:

⚠️ Exam Trap: These limits are testable—questions often present scenarios where exceeding a limit is the root cause of an error.

Common Errors Quick Reference