FAST DOWNLOAD EXAM DP-100 ANSWERS–THE BEST PDF CRAM EXAM FOR DP-100 - RELIABLE VALID BRAINDUMPS DP-100 PPT

Fast Download Exam DP-100 Answers–The Best PDF Cram Exam for DP-100 - Reliable Valid Braindumps DP-100 Ppt

Fast Download Exam DP-100 Answers–The Best PDF Cram Exam for DP-100 - Reliable Valid Braindumps DP-100 Ppt

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Microsoft DP-100 certification exam is an essential tool for professionals who want to enhance their skills in data science and Azure data science solutions. DP-100 exam covers a wide range of topics related to data science solutions on Azure and is designed to test the candidate’s ability to design and implement data science solutions using Azure services. The DP-100 certification exam is an excellent tool for professionals who want to advance their careers in data science and demonstrate their expertise in Azure data science solutions.

Microsoft DP-100 certification exam is a comprehensive exam that covers a wide range of topics related to data science solutions on Azure. DP-100 exam consists of multiple-choice questions and requires the candidate to demonstrate their knowledge and understanding of data science concepts and Azure data science solutions. DP-100 Exam is designed to test the candidate’s ability to design and implement data science solutions on Azure.

Microsoft DP-100 exam is a great way for data scientists to validate their skills and knowledge in Azure data science solutions. Passing DP-100 exam shows that the candidate has the necessary skills to design, implement, and deploy data science solutions on Azure. Moreover, this certification can be a valuable asset for individuals who want to advance their career in the data science field, as it demonstrates their proficiency in various areas related to data science.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q404-Q409):

NEW QUESTION # 404
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. Grid sampling
  • B. Bayesian sampling
  • C. No sampling
  • D. Random sampling
    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)
    }
    )

Answer: B,D

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters


NEW QUESTION # 405
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B


NEW QUESTION # 406
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:


NEW QUESTION # 407
You must store data in Azure Blob Storage to support Azure Machine Learning.
You need to transfer the data into Azure Blob Storage.
What are three possible ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. AzCopy
  • B. Bulk Copy Program (BCP)
  • C. Bulk Insert SQL Query
  • D. Azure Storage Explorer
  • E. Python script

Answer: A,D,E

Explanation:
You can move data to and from Azure Blob storage using different technologies:
* Azure Storage-Explorer
* AzCopy
* Python
* SSIS
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob


NEW QUESTION # 408
You are using a Git repository to track work in an Azure Machine Learning workspace.
You need to authenticate a Git account by using SSH.
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:

1 - Generating a public/private key pair
2 - Add the public key to the Git Account
3 - Clone the Git repository by using an SSH repository URL
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration


NEW QUESTION # 409
......

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