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Microsoft DP-100 Exam

Certification Provider: Microsoft
Exam Name: Designing and Implementing a Data Science Solution on Azure
Duration: 120 Minutes
Number of questions in our database: 251
Exam Version: Jun. 14, 2021
DP-100 Exam Official Topics:
  • Topic 1: Define And Prepare The Development Environment/ Select Development Environment
  • Topic 2: Assess The Deployment Environment Constraints/ Select The Development Environment Analyze And Recommend Tools That Meet System Requirements/ Set Up Development Environment Create An Azure Data Science Environment/ Configure Data Science Work Environments
  • Topic 3: Transform Data Into Usable Datasets/ Develop Data Structures/ Perform Exploratory Data Analysis (Eda)
  • Topic 4: Review Visual Analytics Data To Discover Patterns And Determine Next Steps/ Design A Data Sampling Strategy
  • Topic 5: Design The Data Preparation Flow/ Identify Anomalies, Outliers, And Other Data Inconsistencies
  • Topic 6: Resolve Anomalies, Outliers, And Other Data Inconsistencies/ Standardize Data Formats/ Perform Feature Extraction Algorithms On Numerical Data/ Perform Feature Extraction Algorithms On Non-Numerical Data
  • Topic 7: Select An Algorithmic Approach/ Consider Data Preparation Steps That Are Specific To The Selected Algorithms
  • Topic 8: Determine Appropriate Performance Metrics/ Implement Appropriate Algorithms
  • Topic 9: Determine Ideal Split Based On The Nature Of The Data/ Determine Number Of Splits/ Identify Data Imbalances
  • Topic 10: Determine Relative Size Of Splits/ Resample A Dataset To Impose Balance/ Adjust Performance Metric To Resolve Imbalances

Free Microsoft DP-100 Exam Actual Questions

The questions for DP-100 were last updated On Jun. 14, 2021

Question #1

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?

Reveal Solution Hide Solution
Correct Answer: C

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.


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml

Question #2

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.

Reveal Solution Hide Solution
Correct Answer: C, D

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)

}

)


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

Question #3

You deploy a real-time inference service for a trained model.

The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.

You need to implement a monitoring solution for the deployed model using minimal administrative effort.

What should you do?

Reveal Solution Hide Solution
Correct Answer: B

Configure logging with Azure Machine Learning studio

You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:

1. Sign in to the studio at https://ml.azure.com.

2. Go to Models and select the model you want to deploy.

3. Select +Deploy.

4. Populate the Deploy model form.

5. Expand the Advanced menu.

6. Select Enable Application Insights diagnostics and data collection.


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

Question #4

You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run. Which Python code segment should you use?

A)

B)

C)

D)

Reveal Solution Hide Solution
Correct Answer: A

The get_output method on automl_classifier returns the best run and the fitted model for the last invocation. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.

In [ ]:

best_run, fitted_model = local_run.get_output()


https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb

Question #5

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed.

You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort.

What should you advise the IT Operations team to do?

Reveal Solution Hide Solution
Correct Answer: D

The Azure Machine Learning SDK does not provide support scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure Machine Learning studio. You can only change the node count, not the VM size of the cluster.


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-kubernetes


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