2025 TEST DP-100 DATES | EXCELLENT DESIGNING AND IMPLEMENTING A DATA SCIENCE SOLUTION ON AZURE 100% FREE TRUSTED EXAM RESOURCE

2025 Test DP-100 Dates | Excellent Designing and Implementing a Data Science Solution on Azure 100% Free Trusted Exam Resource

2025 Test DP-100 Dates | Excellent Designing and Implementing a Data Science Solution on Azure 100% Free Trusted Exam Resource

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Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Exam is a certification exam that tests the candidate's knowledge and skills in designing and implementing data science solutions on the Azure platform. DP-100 exam is designed for data scientists, data engineers, and machine learning engineers who work with large amounts of data and want to leverage the power of Azure to build scalable and efficient data solutions.

The Microsoft DP-100 Exam consists of multiple-choice questions and performance-based tasks that assess the candidate's ability to design and implement data science solutions on Azure. Candidates have three hours to complete the exam, and they must achieve a passing score of 700 or higher to become certified.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q52-Q57):

NEW QUESTION # 52
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer

You need to explain the predictions made by the model for all classes by determining the importance of all features.
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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml


NEW QUESTION # 53
You plan to create a speech recognition deep learning model.
The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?

  • A. Rattle
  • B. TensorFlow
  • C. Scikit-learn
  • D. Weka

Answer: B

Explanation:
Explanation
Explanation:
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
Incorrect Answers:
A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.
C: Weka is used for visual data mining and machine learning software in Java.
D: Scikit-learn is one of the most useful library for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Reference:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html


NEW QUESTION # 54
Hotspot Question
You use Azure Machine Learning to train and register a model.
You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.
Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.
You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: AksCompute
Example:
aks_target = AksCompute(ws,"myaks")
# If deploying to a cluster configured for dev/test, ensure that it was created with enough # cores and memory to handle this deployment configuration. Note that memory is also used by # things such as dependencies and AML components.
deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target) Box 2: AksWebservice Box 3: token_auth_enabled=Yes Whether or not token auth is enabled for the Webservice.
Note: A Service principal defined in Azure Active Directory (Azure AD) can act as a principal on which authentication and authorization policies can be enforced in Azure Databricks.
The Azure Active Directory Authentication Library (ADAL) can be used to programmatically get an Azure AD access token for a user.
Incorrect Answers:
auth_enabled (bool): Whether or not to enable key auth for this Webservice. Defaults to True.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes- service
https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/service-prin-aad-token


NEW QUESTION # 55
You run a script as an experiment in Azure Machine Learning.
You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run.
You need to download the log files to a local folder for review.
Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. run.get_file_names()
  • B. run.get_metrics()
  • C. run.get_all_logs(destination='./runlogs')
  • D. run.download_files(output_directory='./runfiles')
  • E. run.get_details()

Answer: C,E

Explanation:
Explanation
The run Class get_all_logs method downloads all logs for the run to a directory.
The run Class get_details gets the definition, status information, current log files, and other details of the run.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)


NEW QUESTION # 56
You create an Azure Machine learning workspace and load a Python training script named tram.py in the src subfolder. The dataset used to train your model is available locally. You run the following script to tram the model:

instructions: 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:


NEW QUESTION # 57
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