Azure Cloud Services Test

This topic evaluates foundational knowledge of the Azure AI ecosystem, including the portfolio of services under Azure Cognitive Services and Azure OpenAI. It assesses familiarity with APIs and SDKs, use case mapping (e.g., text analytics, speech recognition, computer vision), service provisioning, and the ability to choose the right AI service for various business scenarios. Candidates must understand how these services integrate into the broader Azure ecosystem (e.g., through Logic Apps, Azure Functions).

Focuses on the application of Azure Language Services and Azure OpenAI for building language-intelligent applications. Topics include language understanding via LUIS, document summarization, text classification, named entity recognition, and prompt engineering for GPT. Candidates are evaluated on capabilities such as designing prompt-based pipelines, fine-tuning language models, and leveraging Azure for multilingual NLP use cases at scale.

Assesses knowledge of visual data processing using Azure's Vision APIs, including OCR, spatial analysis, object detection, and facial recognition. The topic covers pre-trained APIs (Face API, Image Analysis), custom model training (Custom Vision), and form digitization (Form Recognizer). Candidates must understand performance tuning, image preprocessing, model retraining, and accuracy evaluation strategies in production contexts.

This topic tests expertise in speech-centric AI workloads such as real-time transcription, voice synthesis, and speaker identification. Candidates are assessed on their ability to build voice interfaces using Speech-to-Text, Text-to-Speech, Custom Speech, and Speech Translation. Evaluation includes latency optimization, language model customization, and deploying AI-powered voice solutions integrated with IoT and mobile platforms.

Evaluates proficiency in using Azure Machine Learning for end-to-end model development. This includes configuring compute targets, creating experiments, using AutoML for classification/regression tasks, and analyzing performance metrics. Candidates are expected to understand workspace organization, model registration, and leveraging MLflow tracking for operational transparency.

Tests depth in operationalizing AI/ML projects using Azure DevOps, GitHub Actions, and Azure Machine Learning pipelines. Candidates must demonstrate the ability to create robust CI/CD workflows for model deployment, enable continuous training (CT), and apply model monitoring, drift detection, data versioning, and approval workflows. It emphasizes real-world practices such as model rollback, feature store usage, and secure deployment strategies.

Measures understanding of designing intelligent systems that use Azure AI Search with OpenAI or LLMs for real-time knowledge retrieval. This includes indexing unstructured data, generating vector embeddings, enabling hybrid search, building knowledge-grounded responses, and optimizing query relevance. Candidates must understand how to build scalable RAG pipelines integrated with blob storage, metadata filtering, and chunking strategies.

This topic focuses on applying ethical AI principles at scale using Azure tools. It covers fairness, explainability, transparency, privacy, and bias detection. Candidates are evaluated on implementing interpretability dashboards, using tools like Fairlearn and DiCE, applying Azure Policy for AI compliance, and aligning with regulatory frameworks like the EU AI Act, ISO/IEC 42001, and NIST AI RMF.

Tests the ability to apply enterprise-grade security across Azure AI workloads. Includes managing access using RBAC, Managed Identities, Key Vault for secrets management, data encryption, VNet integration, and private endpoints. Candidates must understand compliance implications and how to implement secure-by-design AI architectures, including secure model endpoints and access policies for AI resources.

Focuses on architectural patterns for deploying enterprise-scale AI applications. Includes AKS-based model serving, scalable microservices, use of Azure Arc for hybrid/multi-cloud AI, and orchestrating AI workloads using containers and event-driven patterns. Advanced topics include cost optimization, disaster recovery, data gravity, federated learning, and enterprise AI platform design for modularity and governance.