Pass the Microsoft Azure DP-100 Questions and answers with ExamsMirror

You manage an Azure Al Foundry project.

You plan to develop a RAG solution from a set of PDF files. To achieve this, you plan to create a vector index from the data. You need to select the location of the data you plan to index.

Which two data sources can you use? Each correct answer presents a complete solution. Choose two. NOTE: Each correct selection is worth one point.

You create a multi-class image classification deep learning model that uses a set of labeled images. You

create a script file named train.py that uses the PyTorch 1.3 framework to train the model.

You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.

You need to define the estimator that will be used to run the script.

Which estimator type should you use?

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 in the review screen.

You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

Solution: Run the following code:

Does the solution meet the goal?

You manage an Azure Al Foundry project.

You plan to evaluate a fine-tuned large language model by doing the following:

• Identifying discrepancies between runs of the same model to pinpoint the areas where adjustments may be needed.

• Verifying the Al-generated responses align with and are validated by the provided context.

You need to identify an evaluation metric and a comparison feature to assess the performance of the model. Which assessment techniques should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You build and manage a model by using Azure Machine Learning workspace.

Before you deploy the model, you must create a Responsible Al dashboard in Azure Machine Learning studio. The dashboard must provide observation of the following:

• metrics that show real-world impact on an outcome of interest due to taking a treatment policy

• examples with minimal changes to a particular data point such that the model's prediction changes

You need to implement the components for the Responsible Al dashboard.

Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component 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.

You have an Azure Machine Learning workspace. You are running an experiment on your local computer.

You need to ensure that you can use MLflow Tracking with Azure Machine Learning Python SDK v2 to store metrics and artifacts from your local experiment runs in the workspace.

In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

You plan to explore demographic data for home ownership in various cities. The data is in a CSV file with the following format:

age,city,income,home_owner

21,Chicago,50000,0

35,Seattle,120000,1

23,Seattle,65000,0

45,Seattle,130000,1

18,Chicago,48000,0

You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results. The experiment must log the following information:

the number of observations in the dataset

a box plot of income by home_owner

a dictionary containing the city names and the average income for each city

You need to use the appropriate logging methods of the experiment’s run object to log the required information.

How should you complete the code? To answer, drag the appropriate code segments to the correct locations. Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

You are managing an Azure Machine Learning workspace.

You must tune a hyperparameter for a neural network model. The learning rate must be a continuous hyperparameter between 0.001 and 0.1. The batch size can be 32.64. or 128.

You need to select the appropriate search space for each parameter.

Which search space should you use? To answer, move the appropriate search spaces to the correct hyperparameters. You may use each search space option 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.

You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.

You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.

learning_rate: any value between 0.001 and 0.1

• batch_size: 16, 32, or 64

You need to configure the sampling method for the Hyperdrive experiment

Which two sampling methods can you use? Each correct answer is a complete solution.

NOTE: Each correct selection is worth one point.

You create an Azure Machine Learning workspace. You train a classification model by using automated machine learning (automated ML) in Azure Machine Learning studio. The training data contains multiple classes that have significantly different numbers of samples.

You must use a metric type to avoid labeling negative samples as positive and an averaging method that will minimize the class imbalance.

You need to configure the metric type and the averaging method.

Which configurations should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.