AI-300 exam questions explained by domain (with answers)

Question 1. An engineer working on a fraud model needs to establish the central resource that manages assets, jobs, compute and endpoints. Options: create or update the Azure Machine Learning workspace; configure a datastore; select and configure a compute target; assign a managed identity the minimum required role. Answer: the workspace. It is the container for everything else; a datastore only references a storage location, a compute target is where jobs run, and the role assignment solves permissions, not the existence of the resource.

Question 2. The dependencies and runtime used by a training or scoring workload must be reproducible. Options: publish the asset to a registry; register a versioned data asset; build and version an Azure Machine Learning environment; package the reusable step as a component. Answer: the environment. A registry shares models, components or environments across workspaces; a data asset pins the dataset version; a component wraps a pipeline step. Only the environment describes the image, packages and versions.

Question 3. A team wants strong candidate algorithms and configurations with limited manual search and the least operational overhead. Options: configure distributed training; create a pipeline job from components; run an AutoML experiment; compare runs by the validation metric in MLflow. Answer: AutoML. Distributed training is for models that exceed a single compute; a pipeline orchestrates dependent steps; MLflow compares what you have already trained. AutoML is the only one that explores algorithms for you.

It is the heaviest domain and the most confusing one, because all its pieces (MLflow, AutoML, hyperparameter tuning, pipelines, endpoints, progressive rollout, data drift) show up as each other's distractors. The way to get them right is to be clear about which problem each one solves.

Question 4. A RAG application in Microsoft Foundry must authorize project access to dependent Azure resources without application secrets. Options: use managed identities and least-privilege RBAC; provision the topology with Bicep and Azure CLI; create and configure the Foundry resource and project; use private networking for the dependencies. Answer: managed identities with RBAC. Bicep reproduces the infrastructure, creating the project is the previous step, and private networking limits where traffic comes from, not who is authorized. What removes the secrets is the managed identity.

Question 5. A prompt, model or application version must not ship when quality gates fail. Options: measure groundedness, relevance, coherence and fluency; automate built-in and custom evaluations in the delivery workflow; run risk and safety evaluators; create a representative test dataset with field mapping. Answer: automate the evaluations in the delivery workflow. The other three are necessary parts (the metrics, the safety evaluators, the dataset), but none of them blocks a release on its own: the evaluation wired into the pipeline as a gate does.

This domain and the optimization one (RAG and fine-tuning) add up to only 20–30%, but they are the ones people coming from classic Azure Machine Learning have practised the least. They deserve time even though they weigh little.

None of them asks for a memorized fact: all of them ask you to pick the action whose main purpose matches the outcome in the scenario. The wrong options are always legitimate actions from the outline that solve a different problem. That is why studying AI-300 as a list of services works badly and studying it as a cycle (provision, train, register, deploy, observe, and the same for generative AI) works well.

The real exam lasts 120 minutes, is English only and needs 700 out of 1,000 to pass. To measure where you stand by domain, practice AI-300 with an explanation on every answer; start free. The full study plan is in how to pass AI-300.