FREE DP-100 DOWNLOAD - NEW DP-100 EXAM REVIEW

Free DP-100 Download - New DP-100 Exam Review

Free DP-100 Download - New DP-100 Exam Review

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Tags: Free DP-100 Download, New DP-100 Exam Review, DP-100 Valid Vce, New DP-100 Test Fee, DP-100 Certification Torrent

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Microsoft DP-100 certification exam is a valuable credential for data professionals who work with Azure technologies and want to validate their skills in designing and implementing data science solutions. DP-100 exam covers a broad range of topics related to data science and Azure services, and candidates can prepare for it by taking training courses and accessing online resources. Passing the DP-100 Exam is a significant achievement that can help data professionals advance their careers and demonstrate their expertise to potential employers.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q71-Q76):

NEW QUESTION # 71
You plan to use .a Deep learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the IXVM to support CUOA
What should you implement?

  • A. Intel Software Guard Extensions (Intel SGX) technology
  • B. Solid State Drives (SSD)
  • C. High Random Access Memory (RAM) configuration
  • D. Computer Processing Unit (CPU) speed increase by using overcloking
  • E. Graphic Processing Unit (GPU)

Answer: B


NEW QUESTION # 72
You are creating data wrangling and model training solutions in an Azure Machine Learning workspace.
You must use the same Python notebook to perform both data wrangling and model training.
You need to use the Azure Machine Learning Python SDK v2 to define and configure the Synapse Spark pool asynchronously in the workspace as dedicated compute How should you complete the rode segment? To answer, select the appropriate options in the answer area.
NOTE: Lach correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 73

You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?

  • A. run.upload_file('row_count', './data.csv')
  • B. run.log('row_count', rows)
  • C. run.log_table('row_count', rows)
  • D. run.tag('row_count', rows)
  • E. run.log_row('row_count', rows)

Answer: B

Explanation:
Log a numerical or string value to the run with the given name using log(name, value, description='').
Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run


NEW QUESTION # 74
Hotspot Question
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

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
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters


NEW QUESTION # 75
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 76
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