- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
- Certification Provider: Microsoft
- Corresponding Certification:Microsoft Certified
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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 2: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Topic 3: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 4: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?
- A. fairlearn.preprocessing.CorrelationRemover
- B. fairiearn.reductions.ExponentiatedGradient
- C. fairlearn.reductions.GridSearch
- D. fairlearn.postprocessing.ThresholdOptimizer
Correct Answer: D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
-
A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Interactive experimentation: Azure Machine Learning compute instance
Scalable training jobs: Azure Machine Learning compute cluster
For interactive experimentation , use an Azure Machine Learning compute instance . Microsoft defines a compute instance as a fully managed, cloud-based development workstation optimized for machine learning development. It integrates directly with Jupyter, JupyterLab, and other development tools in Azure Machine Learning studio, making it the appropriate target for notebook-driven experimentation, iterative coding, debugging, and development. A compute instance is a single-node environment and can be stopped or configured with idle shutdown to control costs.
For scalable training jobs , use an Azure Machine Learning compute cluster . Compute clusters are managed training targets that can scale from a minimum to a configured maximum number of nodes based on job demand. Microsoft specifically recommends them for larger datasets, distributed training, and workloads requiring elastic compute capacity. Setting the minimum node count to 0 allows the cluster to deallocate all worker nodes when no jobs are running, which is an important cost-optimization mechanism.
Azure Batch, Databricks, AKS, and standalone Azure VMs can support specialized workloads, but they do not match the native Azure Machine Learning development-versus-elastic-training pattern as directly as compute instance and compute cluster.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning compute targets, compute instances, compute clusters, autoscaling, distributed training, and cost optimization.
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?
- A. mlflow.log_artifact()
- B. mlflow.log_image()
- C. mlflow.log_metric()
- D. mlflow.log_batch()
Correct Answer: D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?
- A. workspaceblobstore datastore
- B. compute instance
- C. workspacefi lest ore datastore
Correct Answer: B 🗳️
you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer.
Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace.
You need to comminute to log training and artifacts from your data science code.
What should you do?
- A. Instantiate the job class.
- B. Log in to workspace1.
- C. Instantiate the MLClient class.
- D. Configure the tracking URL.
Correct Answer: D 🗳️
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