Free AI-300 Practice Test – 20 Questions with Explanations

A training script runs successfully from a local environment, and the team can authenticate to Azure. However, no run metrics or artifacts appear under the target Azure Machine Learning workspace, and the team wants centralized experiment history without rewriting the training loop.
What should you do first?

You have labeled tabular data and need a strong baseline classifier quickly. The team wants Azure Machine Learning to try multiple algorithms and parameter combinations, apply built-in featurization where appropriate, and compare results against a chosen primary metric before investing in custom training code.
Which approach is the best fit?

A data science team is deciding which existing workspace asset to standardize for tabular retraining. They need a version that best supports DataFrame-style consumption, selective column handling, and a cleaner path to repeatable training runs.

Which asset is the best choice to promote for the retraining workflow?

A workspace owner inherited several datastore definitions and must choose one for a new training workload. The workload reads from an ADLS Gen2 filesystem, the security team does not want secrets stored in the workspace, and the storage administrators will manage authorization through RBAC and ACLs.

Which datastore is the best fit?

A machine learning engineer wants to turn a preprocessing script into a reusable pipeline building block that other teams can consume in Azure Machine Learning. The goal is to define the interface clearly, package code and environment correctly, and then use the component in a pipeline run.

What is the correct order?

A team wants to tune exactly three hyperparameters with Bayesian sampling. They have enough budget for a serious search but only want moderate parallelism so completed trials can inform later choices.

Which configuration is the best fit?

A team is training a large multilingual transformer across multiple GPU nodes in Azure Machine Learning. The codebase already uses PyTorch, the main bottleneck is cross-node communication, and the architects are allowed to choose specialized VM SKUs to improve scaling efficiency.
Which design is the best fit?

An organization runs separate development, test, and production Azure Machine Learning workspaces across multiple regions. The platform team wants approved models, components, environments, and selected data assets to be published once and then consumed across those workspaces without manually recreating them in each workspace.
Which design is the BEST fit?

A platform team stores Azure Machine Learning deployment code in GitHub and wants GitHub Actions to submit jobs and deploy models to a workspace. Security policy forbids long-lived secrets in the repository and requires access to be constrained to the specific repository and deployment branch.
Which configuration should you implement?

A regulated enterprise is standardizing new Azure Machine Learning workspaces through Bicep. Data scientists must reach the workspace only from a hub VNet, and the security team also wants managed compute egress reduced to approved destinations wherever possible.
Which IaC pattern is the best fit?

A recommendation model is hosted on a managed online deployment. Traffic is light overnight but spikes sharply during regional lunchtime peaks, and the team wants elasticity without paying for constant overprovisioning.

Which change best aligns this deployment with the stated scaling requirement?

A lender is introducing a new underwriting model version on a managed online endpoint. The release plan must minimize blast radius, validate the new deployment before broad traffic exposure, and preserve a fast rollback path if production metrics degrade.

What is the correct order?

An insurer must score 4 million claim records every night and deliver results before 4:00 AM. The workload is not interactive, requests arrive as large files rather than per-user API calls, and the operations team wants a managed Azure Machine Learning inference pattern rather than custom orchestration.
Which deployment choice is the best fit?

A platform team wants to measure production classification quality for a model deployed to an online endpoint. Actual labels arrive one day after prediction, and the team wants Azure Machine Learning to compute the metrics instead of relying on manual notebook joins.
Which TWO actions are required? (Select TWO.)

A company is deploying a Semantic Kernel agent through Foundry for internal finance operations. They want continuous monitoring with minimal code changes, a dashboard for token usage and latency, evaluator-based oversight on sampled production traffic, and trace visibility when tool calls behave unexpectedly.
Which design is the best fit?

A product team is iterating on prompts for a multilingual assistant. They want prompt changes to be reviewable, comparable, and easy to evaluate before merge.
Which TWO practices best support that goal? (Select TWO.)

A retailer is comparing embedding models for a multilingual product-support RAG assistant. The production requirement is fewer than 140 ms average retrieval latency, and the business wants the strongest feasible retrieval quality without sacrificing multilingual support.

Which model should the team choose for production first?

A RAG assistant indexes long policy PDFs as very large chunks, and users report that the system often misses the one paragraph that actually contains the answer. The current vector query also applies a strict minimum threshold, so borderline-but-useful passages are often filtered out.
Which TWO changes are the best first tuning actions? (Select TWO.)

A financial-services company needs a repeatable release gate for a new agent. The gate must run before production, use harmful-content evaluators against a maintained test set, support CI/CD, and still allow reviewers to inspect detailed results before approval.
Which approach is the BEST fit?

A financial-services team is building a reasoning-heavy review assistant. Exact gold answers are limited, but the team can define pass/fail logic, combine multiple scoring signals, and optimize toward a complex behavior rather than a single fixed response. They want the customization method that best matches that design.
Which approach is the BEST fit?