DP-100 Free Practice Exams, DP-100 Latest Dumps
DP-100 Free Practice Exams, DP-100 Latest Dumps
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Tags: DP-100 Free Practice Exams, DP-100 Latest Dumps, DP-100 Actual Test Answers, Valid DP-100 Real Test, Customized DP-100 Lab Simulation
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Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is a certification that evaluates a candidate's knowledge of data science, machine learning, and Azure cloud technologies. DP-100 Exam is targeted towards individuals who want to pursue a career in data science and want to showcase their expertise in designing and implementing data solutions on the Azure platform.
Microsoft DP-100 Exam Syllabus Topics:
Topic | Details |
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Topic 1 |
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Topic 2 |
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Topic 3 |
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Topic 4 |
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Skills measured
- Run experiments and train models (20–25%)
- Manage Azure resources for machine learning (25–30%)
- Deploy and operationalize machine learning solutions (35–40%)
- Implement responsible machine learning (5–10%)
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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q169-Q174):
NEW QUESTION # 169
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 train and register a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AciWebservice instance.
Set the value of the ssl_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?
- A. Yes
- B. No
Answer: B
Explanation:
Instead use only auth_enabled = TRUE
Note: Key-based authentication.
Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.
deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,
memory_gb = 1,
auth_enabled = TRUE)
Reference:
https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html
NEW QUESTION # 170
You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 171
You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 172
space and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments.
Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn
NEW QUESTION # 173
You manage an Azure Machine Learning workspace named workspace1 and a Data Science Virtual Machine (DSVM) named DSMV1.
You must an experiment in DSMV1 by using a Jupiter notebook and Python SDK v2 code. You must store metrics and artifacts in workspace 1 You start by creating Python SCK v2 code to import ail required packages.
You need to implement the Python SOK v2 code to store metrics and article in workspace1.
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 the correctly order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 174
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