Microsoft Azure Data Scientist Associate Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Design and prepare a machine learning solution (22.5% of the exam)

Design a machine learning solution

• Identify the structure and format for datasets
• Determine the compute specifications for machine learning workload
• Select the development approach to train a model

Summary: In this section, you will explore how to approach the early stages of designing a machine learning solution. A focus is placed on matching data structures and formats to project goals, ensuring that your datasets align with the type of analysis or prediction your workload requires. Additionally, you will learn how to evaluate compute needs for training, balancing performance, cost efficiency, and scalability.

The section also helps you think strategically about development methodology. You will compare approaches such as custom training versus leveraging automated tools and understand the trade-offs in flexibility, control, and time-to-value. The focus is on making thoughtful decisions that set the foundation for efficient experimentation and deployment later in the lifecycle.

Create and manage resources in an Azure Machine Learning workspace

• Create and manage a workspace
• Create and manage datastores
• Create and manage compute targets
• Set up Git integration for source control

Summary: This portion highlights the importance of establishing a structured and well-managed environment for all machine learning workloads. You will work with Azure Machine Learning workspaces to centralize projects, manage data sources as datastores, and configure compute targets with the right level of power and scalability for workloads.

Additionally, you will see how to align machine learning work with strong source control practices by setting up Git integration. This allows teams to collaborate in a seamless manner while maintaining reproducibility and version history for experimental workflows. Together, these skills ensure that your workspace operates as a secure, professional-grade hub for ongoing data science efforts.

Create and manage assets in an Azure Machine Learning workspace

• Create and manage data assets
• Create and manage environments
• Share assets across workspaces by using registries

Summary: This section introduces the concept of reusable assets that bring consistency and agility into machine learning development. Assets such as curated datasets and predefined environments streamline work while ensuring reproducibility and traceability across experiments. Managing these effectively reduces duplication of effort and supports collaboration between different projects and teams.

The ability to share assets across workspaces through registries also plays a pivotal role. Registries provide a centralized location where models, environments, and datasets can be reused in multiple solutions, improving efficiency and fostering organizational alignment. By mastering asset management, you not only simplify the development process but also accelerate teamwork across the enterprise.

Domain 2: Explore data, and run experiments (22.5% of the exam)

Use automated machine learning to explore optimal models

• Use automated machine learning for tabular data
• Use automated machine learning for computer vision
• Use automated machine learning for natural language processing
• Select and understand training options, including preprocessing and algorithms
• Evaluate an automated machine learning run, including responsible AI guidelines

Summary: In this part, you will discover how automated machine learning (AutoML) helps evaluate and select suitable models for different types of data such as tabular datasets, vision tasks, and natural language processing. The emphasis is on simplifying the model selection process by automatically testing multiple options, reducing the time required to identify a strong baseline.

You will also learn how to interpret AutoML runs with responsible AI principles in mind. This involves evaluating fairness, transparency, and performance metrics to ensure that the models you select are not only accurate but also aligned with ethical AI practices. This balanced approach keeps development both efficient and responsible.

Use notebooks for custom model training

• Use the terminal to configure a compute instance
• Access and wrangle data in notebooks
• Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute
• Retrieve features from a feature store to train a model
• Track model training by using MLflow
• Evaluate a model, including responsible AI guidelines

Summary: Here you will move into custom model training with notebooks, leveraging Python-based workflows to prepare and analyze data. You will practice configuring compute environments, accessing datasets directly from notebooks, and performing interactive exploratory data work. The integration with Synapse Spark pools adds the ability to scale data wrangling for larger and more complex datasets.

Tracking progress is emphasized through MLflow, which captures training runs, hyperparameters, and results. Beyond training, evaluating models through responsible AI guidelines ensures that models deliver insights fairly and ethically. This section helps ground your learning in both technical rigor and a responsible approach to deployment.

Automate hyperparameter tuning

• Select a sampling method
• Define the search space
• Define the primary metric
• Define early termination options

Summary: Hyperparameter tuning adds a layer of optimization to model performance. In this section, you will learn how to set up parameter sweeps with various sampling strategies, design a search space, and select primary metrics to focus your optimization efforts. This systematic approach makes experimentation measurable and efficient.

You will also explore early termination options, which save time and resources by halting underperforming runs. Automating this process ensures a practical balance between computation and performance gains, allowing you to maximize accuracy without unnecessary cost or effort.

Domain 3: Train and deploy models (27.5% of the exam)

Run model training scripts

• Consume data in a job
• Configure compute for a job run
• Configure an environment for a job run
• Track model training with MLflow in a job run
• Define parameters for a job
• Run a script as a job
• Use logs to troubleshoot job run errors

Summary: This section focuses on running scripts efficiently as training jobs within Azure Machine Learning. You will explore how to specify data inputs, define job parameters, and select suitable compute environments. MLflow once again plays a central role in capturing runs, supporting effective organization's tracking for results and metrics.

By scheduling and monitoring jobs, you will also learn how to troubleshoot effectively with logs to catch and correct issues. Running models at this professional scale ensures you can deliver consistent results while maintaining clear diagnostics for complex runs.

Implement training pipelines

• Create custom components
• Create a pipeline
• Pass data between steps in a pipeline
• Run and schedule a pipeline
• Monitor and troubleshoot pipeline runs

Summary: Pipelines add structure and automation to repeated training processes. Here you will focus on creating reusable components and connecting them into powerful pipelines that can automate workflows from preprocessing to final deployment. Connecting these steps with data transfer logic guarantees smooth transitions across the pipeline.

Once pipelines are established, scheduling, monitoring, and troubleshooting runs brings governance and oversight. Mastering pipelines is about operational excellence, ensuring that machine learning solutions are not just experimental but production-ready and repeatable at scale.

Manage models

• Define the signature in the MLmodel file
• Package a feature retrieval specification with the model artifact
• Register an MLflow model
• Assess a model by using responsible AI principles

Summary: Managing trained models properly ensures they can be reused, interpreted, and deployed consistently. You will learn to define clear input and output signatures inside MLmodel files and package feature retrieval specifications for seamless integration into production workflows.

Additionally, you will register models directly into the Azure ML registry to establish firm traceability. As you assess models, applying responsible AI principles adds the critical dimension of fairness and accountability, ensuring your solutions provide accurate and equitable results.

Deploy a model

• Configure settings for online deployment
• Deploy a model to an online endpoint
• Test an online deployed service
• Configure compute for a batch deployment
• Deploy a model to a batch endpoint
• Invoke the batch endpoint to start a batch scoring job

Summary: This section takes you into production deployment, where models must serve predictive insights at scale. You will learn to configure models for both real-time online endpoints and batch deployment scenarios. Testing endpoints ensures reliability and responsiveness before full-scale use.

Batch endpoints, meanwhile, support high-volume processing of prediction requests in a way that balances throughput and efficiency. Mastery here allows you to bring trained models directly to business users with confidence in their performance and maintainability.

Domain 4: Optimize language models for AI applications (27.5% of the exam)

Prepare for model optimization

• Select and deploy a language model from the model catalog
• Compare language models using benchmarks
• Test a deployed language model in the playground
• Select an optimization approach

Summary: In this section, you focus on modern language models and their deployment for AI-powered applications. You will practice deploying models directly from Azure’s catalog and comparing their performance by reviewing benchmarks aligned to accuracy and efficiency.

Testing deployed models in the playground allows for interactive validation and scenario-based experimentation. With results in mind, you select the most effective optimization approach, aligning model performance with unique workload demands.

Optimize through prompt engineering and prompt flow

• Test prompts with manual evaluation
• Define and track prompt variants
• Create prompt templates
• Define chaining logic with the prompt flow SDK
• Use tracing to evaluate your flow

Summary: Prompt engineering and prompt flow optimization elevate the responsiveness and accuracy of large language models. You will explore creating and refining prompt variants, develop reproducible templates, and track each version for effectiveness.

The prompt flow SDK helps define logical chains of prompts, while tracing tools provide actionable evaluation insights. These skills make your workflows more structured, scalable, and measurable so your AI applications deliver reliable results.

Optimize through Retrieval Augmented Generation (RAG)

• Prepare data for RAG, including cleaning, chunking, and embedding
• Configure a vector store
• Configure an Azure AI Search-based index store
• Evaluate your RAG solution

Summary: Retrieval Augmented Generation (RAG) extends model capabilities by grounding its outputs with external knowledge sources. In this section, you will prepare datasets for embedding, clean and chunk information, and configure vector stores to support retrieval.

You will also implement Azure AI Search as an index store and evaluate the end-to-end pipeline. This workflow supports creating highly accurate and context-aware AI applications tailored for specialized domains.

Optimize through fine-tuning

• Prepare data for fine-tuning
• Select an appropriate base model
• Run a fine-tuning job
• Evaluate your fine-tuned model

Summary: Fine-tuning allows precise customization of models for domain-specific tasks. You will learn how to structure datasets for fine-tuning, select the appropriate foundation models, and run jobs that extend their performance toward specific objectives.

Evaluation then ensures that fine-tuned models provide superior results compared to general-purpose alternatives. With this knowledge, you become able to deliver custom AI solutions that fully leverage the power of Azure’s modern ecosystem.