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Microsoft DP-100 exam is an essential certification for data professionals who want to develop their skills in designing and implementing data solutions using Microsoft Azure. Designing and Implementing a Data Science Solution on Azure certification validates the candidate's knowledge of various aspects of data science, including data exploration, data preparation, data modeling, machine learning, and data visualization. The DP-100 Certification is recognized globally and can help the candidate to gain better job opportunities and career growth.
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The DP-100 Exam consists of a series of multiple-choice questions that test a candidate's knowledge of data science concepts and their ability to apply these concepts in real-world scenarios. DP-100 exam is designed to be challenging, and candidates are encouraged to spend ample time preparing before attempting the exam. Microsoft offers a variety of training resources to help candidates prepare, including online courses, study guides, and practice exams. Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q367-Q372):NEW QUESTION # 367
Hotspot Question
You are tuning a hyperparameter for an algorithm.
The following table shows a data set with different hyperparameter, training error, and validation errors. Answer:
Explanation:
Explanation:
Box 1: 4
Choose the one which has lower training and validation error and also the closest match.
Minimize variance (difference between validation error and train error).
Box 2: 5
Minimize variance (difference between validation error and train error).
Reference: https://medium.com/comet-ml/orga ... project-management- guidelines-2d2b85651bbd
NEW QUESTION # 368
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?
A. No
B. Yes
Answer: A
Explanation:
Explanation
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference: https://docs.microsoft.com/en-us ... -train-scikit-learn
NEW QUESTION # 369
You create a script for training a machine learning model in Azure Machine Learning service.
You create an estimator by running the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point. Answer:
Explanation:
Explanation:
Box 1: Yes
Parameter source_directory is a local directory containing experiment configuration and code files needed for a training job.
Box 2: Yes
script_params is a dictionary of command-line arguments to pass to the training script specified in entry_script.
Box 3: No
Box 4: Yes
The conda_packages parameter is a list of strings representing conda packages to be added to the Python environment for the experiment.
NEW QUESTION # 370
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point. Answer:
Explanation:
Explanation:
Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:
Tabular Explainer: Used with tabular datasets.
Text Explainer: Used with text datasets.
Image Explainer: Used with image datasets.
Box 1: Tabular
Box 2: Text
Box 3: Image
Reference: https://medium.com/microsoftazur ... arning-d07975741298
NEW QUESTION # 371
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. Answer:
Explanation:
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