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How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-100&examname=Exam%20DP-100:%20Designing%20and%20Implementing%20a%20Data%20Science%20Solution%20on%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-100

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Testing Process Details of Microsoft DP-100

As for the exam format, there are usually about 40-60 multiple-choice, active screen, best answer, hot area, build lists, labs, mark review and other question styles. You are expected to complete them in 3 hours. The passing score is 700 out of 1000 maximum possible points. The test is available in the following languages: English, Japanese, Korean, and Chinese (Simplified). To schedule your test, you should first pay the registration fee of $165 and go to the Pearson VUE platform to create an account. Once you log in, choose the “Proctored Exams” section and enter ‘DP-100' as the exam code. Follow the prompts to complete registration. At this stage, you can choose to take your test offline or online. Notice, though, that if you don't succeed on the first try, you'll be required to pay the enrollment fee for the next attempt as well.

Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure is a certification exam designed for data professionals who want to demonstrate their skills in designing and implementing data science solutions on Azure. It is intended for individuals who work with data engineers, data scientists, and other stakeholders in the organization to create and deploy end-to-end data science solutions.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q403-Q408):

NEW QUESTION # 403
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.
An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and then run the training script as an experiment on local compute.

A. No
B. Yes

Answer: A

Explanation:
Explanation
Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

NEW QUESTION # 404
You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.
The training dataset that you are using is highly unbalanced.
You need to evaluate the classification model.
Which primary metric should you use?

A. normalized_root_mean_squared_error
B. accuracy
C. normalized_mean_absolute_error
D. [spearman_correlation
E. AUC.weighted

Answer: E

Explanation:
Explanation
AUC_weighted is a Classification metric.
Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml

NEW QUESTION # 405
You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line.
Which metric should you review?

A. Coefficient of determination
B. Precision
C. Mean absolute error
D. Root Mean Square Error
E. Recall

Answer: A

Explanation:
Explanation
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION # 406
You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run 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: No
If a training cluster already exists it will be used.
Box 2: Yes
The wait_for_completion method waits for the current provisioning operation to finish on the cluster.
Box 3: Yes
Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.
Box 4: No
Need to use training_compute.delete() to deprovision and delete the AmlCompute target.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

NEW QUESTION # 407
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.

A. Bayesian sampling
B. No sampling
C. Random sampling
D. Grid sampling

Answer: A,C

Explanation:
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Incorrect Answers:
B: Grid sampling can be used if your hyperparameter space can be defined as a choice among discrete values and if you have sufficient budget to exhaustively search over all values in the defined search space. Additionally, one can use automated early termination of poorly performing runs, which reduces wastage of resources.
Example, the following space has a total of six samples:
from azureml.train.hyperdrive import GridParameterSampling
from azureml.train.hyperdrive import choice
param_sampling = GridParameterSampling( {
"num_hidden_layers": choice(1, 2, 3),
"batch_size": choice(16, 32)
}
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

NEW QUESTION # 408
......

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Valid Microsoft Reliable DP-100 Study Plan and Excellent DP-100 Exam Tutorials

How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?

100% Pass Quiz 2025 DP-100: Authoritative Reliable Designing and Implementing a Data Science Solution on Azure Study Plan

Testing Process Details of Microsoft DP-100

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q403-Q408):

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