HOT STANDARD DP-100 ANSWERS | HIGH-QUALITY DP-100: DESIGNING AND IMPLEMENTING A DATA SCIENCE SOLUTION ON AZURE 100% PASS

Hot Standard DP-100 Answers | High-quality DP-100: Designing and Implementing a Data Science Solution on Azure 100% Pass

Hot Standard DP-100 Answers | High-quality DP-100: Designing and Implementing a Data Science Solution on Azure 100% Pass

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Tags: Standard DP-100 Answers, Actual DP-100 Test, Reliable DP-100 Exam Camp, Reliable DP-100 Test Simulator, DP-100 Exam Sample Questions

Once you have practiced on our Designing and Implementing a Data Science Solution on Azure test questions, the system will automatically memorize and analyze all your practice. You must finish the model test in limited time. There have a timer on the right of the interface. Once you begin to do the exercises of the DP-100 test guide, the timer will start to work and count down. If you don’t finish doing the exercises, all your exercises of the DP-100 Exam Questions will be delivered automatically. Then the system will generate a report according to your performance. You will clearly know where you are good at or not.

The DP-100 Exam is an excellent opportunity for data scientists and engineers to validate their skills and gain a competitive advantage in the job market. Designing and Implementing a Data Science Solution on Azure certification provides an added advantage for organizations to identify skilled data scientists and engineers who can design and implement data science solutions on Azure.

Target Audience & Requirements

The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.

>> Standard DP-100 Answers <<

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Free update for 365 days for DP-100 study guide materials is available. That is to say, in the following year, you can get the latest information of the exam for free. Besides, our system will send the latest version of DP-100 exam dumps to your email automatically. And you just need to receive them and carry on your practice. With the experienced experts to compile DP-100 Study Guide materials, the quality can be guaranteed. And if you choose us, we will help you pass the exam successfully, and obtaining a certificate isn’t a dream.

Microsoft DP-100: Preparation Process

Thorough preparation is an important part of the Microsoft DP-100 Exam and its associate-level certification. Here is the ultimate guide to prepare for this test with great deliberation.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q385-Q390):

NEW QUESTION # 385
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 are using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

  • A. No
  • B. Yes

Answer: A

Explanation:
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines


NEW QUESTION # 386
You use the Azure Machine learning SDK foe Python to create a pipeline that includes the following step:
The output of the step run must be cached and reused on subsequent runs when the source.directory value has not changed.
You need to define the step.
What should you include in the step definition?

  • A. hash_path
  • B. allow.reuse
  • C. data-as_input(name-..)
  • D. version

Answer: B


NEW QUESTION # 387
You use an Azure Machine Learning workspace.
You create the following Python 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
Environment is a required parameter. The environment to use for the run. If no environment is specified, azureml.core.runconfig.DEFAULT_CPU_IMAGE will be used as the Docker image for the run.
The following example shows how to instantiate a new environment.
from azureml.core import Environment
myenv = Environment(name="myenv")
Box 2: Yes
Parameter compute_target: The compute target where training will happen. This can either be a ComputeTarget object, the name of an existing ComputeTarget, or the string "local". If no compute target is specified, your local machine will be used.
Box 3: Yes
Parameter source_directory. A local directory containing code files needed for a run.
Parameter script. The file path relative to the source_directory of the script to be run.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment


NEW QUESTION # 388
You are data scientist building a deep convolutional neutral network (CNN) for image classification.
The CNN model you build shows signs of overfitting.
You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Add L1/L2 regularization.
  • B. Add an additional dense layer with 64 input units.
  • C. Use training data augmentation.
  • D. Add an additional dense layer with 512 input units.
  • E. Reduce the amount of training data.

Answer: A,E

Explanation:
B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.
Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.
Three different regularizer instances are provided; they are:
* L1: Sum of the absolute weights.
* L2: Sum of the squared weights.
* L1L2: Sum of the absolute and the squared weights.
D: Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.
By avoiding training all nodes on all training data, dropout decreases overfitting.
References:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/
https://en.wikipedia.org/wiki/Convolutional_neural_network


NEW QUESTION # 389

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

Answer: A

Explanation:
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 # 390
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Actual DP-100 Test: https://www.examdumpsvce.com/DP-100-valid-exam-dumps.html

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