Microsoft DP-100 Designing and Implementing a Data Science Solution on Azure Exam Practice Test

You have the following Azure subscriptions and Azure Machine Learning service workspaces:

You need to obtain a reference to the mi-protect workspace

Solution: Run the following Python code.

Does the solution meet the goal?

You are performing clustering by using the K-means algorithm.

You need to define the possible termination conditions.

Which three conditions can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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 are analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Remove the entire column that contains the missing data point.

Does the solution meet the goal?

You plan to run a Python script as an Azure Machine Learning experiment.

The script contains the following code:

import os, argparse, glob

from azureml.core import Run

parser = argparse.ArgumentParser()

parser.add_argument('--input-data',

type=str, dest='data_folder')

args = parser.parse_args()

data_path = args.data_folder

file_paths = glob.glob(data_path + "/*.jpg")

You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source.

You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.

Which code segment should you use?

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?

You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance. You need to select an early termination policy that meets the following requirements:

* accounts for the performance of all previous runs when evaluating the current run

* avoids comparing the current run with only the best performing run to date

Which two early termination policies should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

You have fine-tuned an Azure OpenAI Service model by using the Azure Ai Foundry portal. The fine-tuned model is overfitting.

You plan to correct overfitting by fine-tuning the model again

You need to modify the default value of a fine-tuning task parameter to minimize the possibility of overfitting. Which modification should you apply?