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Azure DP-100 Cheat Sheet

April 24, 2026

Azure DP-100 cheat sheet for key facts, traps, service mappings, and final review.

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Use this cheat sheet for Microsoft Certified: Azure Data Scientist Associate (DP-100) after you know the basics but before you start a timed practice block. The goal is not to memorize a vendor catalog; the goal is to classify the scenario and reject attractive wrong answers quickly.

DP-100 answer sequence

Use this when the stem mixes MLOps infrastructure, model lifecycle, GenAIOps, or monitoring.

    flowchart TD
	  S["Scenario"] --> I["Classify the AI operation"]
	  I --> L["Track lifecycle or infrastructure"]
	  L --> Q["Check quality and observability"]
	  Q --> O["Check optimization or rollback"]

First-pass question triage

  1. Name the tested lane before reading the answer choices.
  2. Underline the constraint: security, cost, reliability, latency, governance, implementation effort, or evidence.
  3. Reject answers that solve a neighboring problem but not the stated requirement.
  4. Prefer the smallest correct control, service, workflow, or command that satisfies the constraint.
  5. Look for proof: logs, tests, metrics, policy evidence, deployment status, evaluation results, or user-visible recovery.

What to know cold

Lane Decision rule Reject when
MLOps infrastructure Provision workspaces, compute, registries, environments, identity, networking, and IaC for repeatable ML operations. Treating notebook success as production readiness.
Model lifecycle Track data, training, evaluation, registration, deployment, monitoring, rollback, and retraining signals. Ignoring lineage, drift, or deployment approval because the model metric looked good once.
GenAIOps infrastructure Operationalize prompts, agents, retrieval, model endpoints, evaluations, and content-safety controls. Managing GenAI apps like static code without prompt, retrieval, and model behavior checks.
Quality and observability Measure relevance, groundedness, safety, latency, cost, drift, and incident signals. Relying on ad hoc manual testing for production AI behavior.
Optimization Tune model choice, prompt design, retrieval, batching, caching, endpoint shape, and cost-performance trade-offs. Scaling capacity before proving the bottleneck or quality failure.

Common traps and better instincts

Trap Better instinct
Confusing DevOps with MLOps Add data, model, experiment, and drift controls to normal CI/CD thinking.
No lineage Prefer answers that preserve dataset, code, environment, model, and evaluation traceability.
No GenAI regression checks Use repeatable evaluations when prompts, retrieval indexes, tools, or models change.
Tuning before measuring Use telemetry and evaluation results before changing models or capacity.

Final 15-minute review

If the stem says Start with
least privilege, private access, compliance, or audit identity scope, data boundary, policy enforcement, logging, and ownership
least operational effort managed service, native integration, simple workflow, and fewer moving parts
high availability, recovery, or outage failure domain, recovery objective, health check, rollback, and validation
performance, scale, or cost bottleneck evidence, traffic pattern, sizing, caching, batching, and quotas
troubleshoot, diagnose, or investigate symptom, recent change, logs, metrics, status, dependency, and smallest safe test

Practice fit

Use IT Mastery for the exact product route, practice status, spaced review when available, and close-answer explanation practice as coverage expands.

Open the exact IT Mastery route here: DP-100 on MasteryExamPrep.

Decision order

If the stem says production AI, answer with repeatability, lineage, evaluation, monitoring, rollback, and ownership.

Revised on Sunday, May 10, 2026
Study Plan