Microsoft Certified: Azure Data Scientist Associate Advanced Practice Exam: Hard Questions 2025

You are designing an Azure Machine Learning solution for a bank. Training must occur in a locked-down Azure VNet with no public IPs. Data lives in an ADLS Gen2 account with public network access disabled. Your data scientists use Azure ML studio and notebooks. They can create compute but all dataset reads fail with authorization/network errors. You need a design that preserves network isolation while enabling training jobs to read ADLS data securely with minimal operational overhead. What should you implement?

Your team runs hundreds of hyperparameter sweeps per week in Azure ML. You notice that many runs recompute the same feature engineering steps even when input data and code are unchanged, causing long queues and wasted GPU time. You need to maximize reuse across jobs while ensuring correctness (no stale features when code or data changes). Which approach best addresses this in Azure ML v2?

You are building a fraud model with severe class imbalance (0.2% positive). Business stakeholders care most about capturing fraud, but also require that the false-positive rate remain below 1% due to manual review costs. You are using Azure ML AutoML for classification. Which configuration most directly aligns the training objective to the constraint?

A regression model is trained on time-series data. During offline evaluation it looks strong, but after deployment the error spikes for recent months. Investigation shows the train/validation split was random and mixed future data into training. You must redesign the evaluation so it reflects production behavior and prevents leakage while still supporting hyperparameter tuning in Azure ML. What should you do?

You train an XGBoost model in Azure ML. In training, you compute target encoding for high-cardinality categorical features. The deployed model shows a major performance drop because the encoding mapping differs between training and inference. You need a robust approach that guarantees training and scoring use the identical transformations and that the full pipeline is versioned. What should you do?

You run distributed training on a GPU cluster using Azure ML jobs. Runs are occasionally non-reproducible: metrics vary significantly between re-runs even when using the same code. You must improve reproducibility without sacrificing distributed performance. What is the best action?

A model is packaged as an MLflow model and deployed to an Azure ML managed online endpoint. The scoring script works locally but fails in the endpoint with missing system libraries required by a Python package dependency. You need to fix the deployment reliably and make future deployments consistent across environments. What should you do?

You must deploy a model to a regulated production environment. Requirements: (1) no inbound public access; (2) only an API Management (APIM) instance in a hub VNet can call the model; (3) all access must be authenticated with Microsoft Entra ID; (4) minimize secret management. Which deployment design best meets these requirements with Azure ML online endpoints?

After deploying a new version of a model to a managed online endpoint, you use a 90/10 traffic split for canary testing. Within minutes, error rates increase only for the canary deployment, but average endpoint metrics look acceptable because the stable deployment dominates traffic. You need fast detection and automatic rollback based on deployment-specific signals. What should you implement?

A batch scoring pipeline writes predictions to ADLS daily. Downstream teams report inconsistent results: the same input record sometimes yields different outputs across days even when the model version is unchanged. You suspect silent changes in preprocessing code and/or reference data used during feature joins. You need end-to-end lineage and the ability to reproduce any day’s outputs. What is the best solution in Azure ML?