GitHub Actions<\/a> exercise should leave a traceable run history and a clear failure point.<\/p>\nLab three: track model experiments with MLflow<\/h3>\n
Train a small model several times, record parameters and metrics, and compare results. Keep training data or feature context identifiable enough that you can reproduce the chosen model.<\/p>\n
Then register the selected model instead of relying on a notebook-local artifact.<\/p>\n
Lab four: deploy and test real-time and batch inference<\/h3>\n
Deploy or model both endpoint types for the same model. Compare latency, throughput, cost, and operational behavior.<\/p>\n
Introduce a controlled bad version and rehearse progressive rollout plus rollback.<\/p>\n
Lab five: detect drift and create an operational trigger<\/h3>\n
Change the production input distribution or simulate drift, then define which metric should reveal the change. Add an alert or retraining trigger concept.<\/p>\n
The lab should distinguish drift detection from automatic retraining approval.<\/p>\n
Lab six: deploy a Foundry model with controlled identity and network<\/h3>\n
Create a Foundry project, choose a foundation model, configure an endpoint, and document RBAC, managed identity, and private-network choices. Compare serverless and managed-compute implications.<\/p>\n
Infrastructure ownership should be as explicit as it was in the Machine Learning lab.<\/p>\n
Lab seven: version prompts and compare variants<\/h3>\n
Create two prompt variants for one task, store them in Git, and run both against a small fixed evaluation dataset. Record which version is deployed.<\/p>\n
Prompt changes should be reviewed as production changes because they can alter behavior without changing the model.<\/p>\n
Lab eight: evaluate GenAI quality and safety<\/h3>\n
Measure groundedness, relevance, coherence, fluency, safety or harmful-content signals, and latency. Add at least one human review where an automatic metric is not sufficient.<\/p>\n
Record the threshold that would block release or trigger investigation.<\/p>\n
Lab nine: tune a RAG pipeline<\/h3>\n
Change chunk size, similarity threshold, embedding choice, or hybrid retrieval strategy one factor at a time. Use a fixed question set and relevance measures to compare the result.<\/p>\n
Do not change the foundation model until retrieval evidence shows that generation rather than retrieval is the limiting layer.<\/p>\n
Lab ten: fine-tune and operate one model lifecycle<\/h3>\n
Use a small training or synthetic-data scenario to understand fine-tuning, versioning, evaluation, deployment, monitoring, and rollback. Track cost and token\/resource usage through the lifecycle.<\/p>\n
Add a registry exercise after the Machine Learning workspace lab. Publish or conceptually share one environment or component across two projects and then change its version. Observe how reusable assets can standardize deployments while still requiring explicit version control. Organization-level reuse should not silently change every consuming project.<\/p>\n
Add a data-drift versus model-performance comparison. Change input distribution without changing labels and then compare that with a case where prediction quality drops while inputs look similar. The distinction helps candidates understand why drift is a diagnostic signal rather than a complete explanation of model quality.<\/p>\n
Add a feature-retrieval specification or feature consistency note when registering the traditional model. Record which features the model expects and how serving retrieves or constructs them. Training-serving inconsistency is easier to prevent when feature requirements travel with the model artifact.<\/p>\n
Add a network-security test by placing one workspace or Foundry dependency behind restricted networking and documenting what must resolve and authenticate for the workload to function. Then compare a network-denied error with an RBAC-denied error. The symptoms can be similar while the owning control is different.<\/p>\n
Add provisioned throughput or capacity planning to the Foundry lab. Estimate requests, token volume, and expected peak behavior, then choose whether flexible serverless consumption or provisioned capacity fits. Record what monitoring signal would tell you that the choice needs revision.<\/p>\n
Add distributed tracing to the RAG lab. Capture at least conceptual spans for user request, retrieval, model call, and response. If latency increases, identify which span grew. If quality drops, attach the retrieved context and model response. Tracing turns an opaque AI interaction into an inspectable production path.<\/p>\n
Add a safety regression test. Include a small set of prompts designed to expose harmful or policy-violating output and run them before and after prompt or model changes. Release criteria should prevent a quality improvement in one dimension from silently creating unacceptable behavior in another.<\/p>\n
Add cost to the evaluation report. Record token use, endpoint capacity or request cost, and infrastructure consumption alongside quality metrics. An AI system that improves relevance by a tiny amount while multiplying cost may not be a better production system.<\/p>\n
Add one controlled prompt rollback. Deploy a new prompt, observe a regression on the fixed evaluation set, restore the previous prompt version, and confirm recovery. This lab proves that prompt versioning is an operational control rather than only a collaboration convenience.<\/p>\n
Close the entire environment with a handoff test. Give another engineer the repository, deployment workflow, model\/prompt versions, evaluation dataset, dashboards, and rollback notes. If they can reproduce and operate both the ML and GenAI systems without your memory, the lab reflects the lifecycle discipline AI-300 is designed to assess.<\/p>\n
Add one registry reuse lab by sharing a tested environment or component between two Machine Learning workspaces. Change the asset version and verify that consumers can choose the intended release instead of being forced onto the latest change. This demonstrates organization-level reuse without uncontrolled coupling.<\/p>\n
Add one distributed-training thought experiment even if the lab uses a small model. Identify what changes when training spans multiple nodes or accelerators: data access, synchronization, environment consistency, cost, and failure behavior. AI-300 expects conceptual fluency with large-model training operations even when hands-on practice stays small.<\/p>\n
Add an automated evaluation step to the GenAI release workflow. A pull request or deployment candidate should run the fixed evaluation set, calculate quality and safety metrics, and fail if a required threshold is missed. This is the GenAIOps equivalent of adding tests before production promotion.<\/p>\n
Add one cost regression. Increase context size or use a more expensive model and compare token consumption and response time with quality gain. Decide whether the new version is operationally justified. Cost optimization is easier to remember once it is connected to a real quality trade-off.<\/p>\n
Finish with an incident drill where the user reports \u201cthe AI is bad\u201d but the actual cause is retrieval, RBAC, network latency, or a prompt version. Use traces and logs to identify the layer. That exercise reflects the practical value of AI-300: turning an opaque AI complaint into an inspectable production system.<\/p>\n
Add an asset-reproducibility exercise. Delete or recreate a development environment from source and verify that compute, environment dependencies, model assets, prompts, and deployment configuration can be restored without undocumented manual state. Rebuildability is one of the clearest indicators that MLOps and GenAIOps practices are actually working.<\/p>\n
Add a progressive rollout drill for the GenAI application, not only the classic model. Route a limited test audience or traffic slice to a new prompt\/model\/retrieval configuration, compare evaluation and runtime metrics, and define the stop condition. Release control applies to generative systems just as strongly as to predictive models.<\/p>\n
Add a production-cost alert with a realistic threshold. A spike in tokens or provisioned capacity may indicate legitimate demand, an inefficient prompt, retrieval expansion, or abuse. The lab should teach you to investigate cause before simply lowering the limit.<\/p>\n
Finish with a short post-incident report for one simulated failure. Include what changed, which signal detected it, which version was affected, how service was restored, and what preventive control was added. This connects the technical objectives to an operational habit that survives beyond the exam.<\/p>\n
Add an environment-drift exercise. Change a package or dependency in one development environment without updating the source definition, then reproduce the workspace elsewhere. The mismatch demonstrates why versioned environments and components matter for both training reproducibility and endpoint behavior.<\/p>\n
Add one human-approval gate to a fine-tuning or GenAI deployment. Automated evaluation may pass, but a high-risk application can still require review before production. Document who approves, what evidence they inspect, and how that decision is recorded.<\/p>\n
Add a synthetic-data review. Generate examples for a fine-tuning task, then sample them for factual quality, diversity, harmful patterns, and duplication. Synthetic data is useful only when the generated training set improves the task without amplifying systematic defects.<\/p>\n
Finally, export the runbook and evaluation evidence as if the original engineer were leaving the team. If the next operator can identify the deployed infrastructure, model, prompt, retrieval configuration, cost baseline, and rollback path, the hands-on environment demonstrates the operational maturity behind the certification.<\/p>\n
Keep one final deployment manifest listing the infrastructure commit, model version, prompt version, retrieval configuration, evaluation dataset version, and endpoint. That single record makes rollback and post-incident analysis far more reliable.<\/p>\n
Finish with a runbook covering infrastructure version, model version, prompt version, endpoint, evaluation metrics, observability, and rollback. That end-to-end evidence is the practical core of AI-300.<\/p>\n","protected":false},"excerpt":{"rendered":"
Hands-on AI-300 preparation should use one traditional ML workload and one generative AI application so the differences and shared operational controls are obvious. The current exam expects candidates to configure infrastructure, automate deployment, manage model lifecycle, evaluate quality, observe production behavior, optimize RAG, and manage fine-tuning. Small labs are enough if each one produces evidence […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/26351"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=26351"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/26351\/revisions"}],"predecessor-version":[{"id":26352,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/26351\/revisions\/26352"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=26351"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=26351"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=26351"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}