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| 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 | - Responsible AI design
|
| Topic 3: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 4: Operationalizing machine learning solutions | - Deployment and monitoring
|
>> New Microsoft AI-300 Test Topics <<
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NEW QUESTION # 37
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to set up logging and tracking experiments by using MLflow Tracking.
You need to log the accuracy as a numerical value and the training loss as a plot.
How should you complete the commands? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: log_metric("log", num1)
Log Numerical Metrics
To track a single numerical value that changes over time (like accuracy or evaluation scores), you use the mlflow.log_metric() function. This enables Azure Machine Learning to plot the value on a performance graph automatically.
Box 2: log_artifact(img1)
Log Plots and FilesTo save external files, images, or plots generated during training, you use the mlflow.log_artifact() function. This uploads the specified file (in this case, your saved Matplotlib image) to the run's artifact storage.
Reference:
https://levelup.gitconnected.com/mlflow-made-easy-your-beginners-guide-bf63f8fed915
NEW QUESTION # 38
Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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.
Answer:
Explanation:
NEW QUESTION # 39
An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads.
The organization has the following workload requirements:
* One workload must be real-time, latency-sensitive, and have predictable global usage patterns that demand consistent performance.
* One workload must have variable performance and be optimized for cost-efficient operation.
You need to select a global deployment type for each workload.
Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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.
Answer:
Explanation:
Explanation:
Real-time, predictable: Global Provisioned
Flexible, cost efficient: Global Standard
For the real-time, latency-sensitive workload with predictable global demand , use Global Provisioned .
Microsoft Foundry defines Global Provisioned deployments as providing reserved model processing capacity and predictable throughput through provisioned throughput units (PTUs). Because capacity is reserved, this deployment type provides lower and more consistent latency than Global Standard and is specifically recommended for sustained, predictable, high-throughput production workloads.
For the workload requiring flexibility and cost-efficient operation , use Global Standard . Global Standard operates on a pay-per-token model and dynamically routes requests across Azure ' s global infrastructure.
Microsoft recommends it as the starting deployment type for most workloads because it offers the lowest price, broad regional availability, and high default quota without requiring the customer to reserve PTU capacity. This makes it appropriate when demand varies and paying for permanently reserved throughput would be inefficient.
The architectural distinction is therefore straightforward: Global Provisioned trades reserved-capacity cost for predictable throughput and reduced latency variance, while Global Standard provides elastic, consumption-based operation with greater latency variability under sustained high load. Microsoft's deployment overview likewise identifies provisioned throughput for predictable low-latency performance and standard deployment for general hosted workloads.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Microsoft Foundry deployment types, Global Standard, Global Provisioned, PTUs, latency predictability, throughput, and cost optimization.
NEW QUESTION # 40
Drag and Drop Question
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute1 by using a terminal window from workspace1.
You create a file named "requirements.txt" containing Python dependencies to include Jupyter.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: conda create -n "python_env"
1. Create the environment
conda create -n python_env python=3.10 -y
Step 2: conda activate "python_env"
2. Activate it
conda activate python_env
Step 3: pip install -r "requirements.txt"
3. Install your dependencies (including ipykernel)
pip install -r requirements.txt
Step 4: ipython kernel install --user --name="python_env"
4. Register the environment as a Jupyter kernel
ipython kernel install --user --name="python_env"
NEW QUESTION # 41
-
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.
Answer:
Explanation:
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.
NEW QUESTION # 42
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