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 | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?
- A. Specify the compute name by using the compute parameter of the command job
- B. Initialize AmICompute class with size and type specification.
- C. Initialize and specify the ResourceConfiguration class
- D. Configure the tier parameter to Dedicated VM.
Correct Answer: C 🗳️
You manage an Azure Machine Learning workspace named workspace!.
You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2.
You must transform the Python code into a YAML specification that can be processed by the pipeline service.
You need to import the Python library that provides the transformation functionality.
Which Python library should you import?
- A. azure.ai ml.automl
- B. sklearn
- C. azure.ai.ml.entities
- D. mldesigner
Correct Answer: D 🗳️
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
- A. JSONL
- B. CSV
- C. JSON
- D. TSV
Correct Answer: A 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
You manage an Azure Machine Learning workspace that includes a batch endpoint. You plan to deploy a model to the batch endpoint. You need to configure compute for the deployment. Which compute should you use?
- A. Azure Databricks
- B. Remote VM
- C. Azure Batch
- D. Kubernetes cluster
Correct Answer: A 🗳️
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.
The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python train.py --training_data training_data
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
Explanation: Only visible for ActualVCE members. You can sign-up / login (it's free).
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