Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 21:

  DRAG DROP

  You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.

  Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  Step 1: Sweep Clustering

  Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.

  One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.

  Step 2: Train Model

  Step 3: Evaluate Model

  Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

  You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the

  Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

  References: http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

• Question 22:

  DRAG DROP

  You need to implement early stopping criteria as stated in the model training requirements.

  Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.

  NOTE: More than one order of answer choices is correct. You will receive the credit for any of the correct orders you select.

  Select and Place:

  

  Correct Answer:

  

  Step 1: from azureml.train.hyperdrive

  Step 2: Import TruncationCelectionPolicy

  Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.

  Scenario: You must configure hyperparameters in the model learning process to speed the learning phase. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and

  resources towards models that are more likely to be successful.

  Step 3: early_terminiation_policy = TruncationSelectionPolicy..

  Example:

  from azureml.train.hyperdrive import TruncationSelectionPolicy

  early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)

  In this example, the early termination policy is applied at every interval starting at evaluation interval 5. A run will be terminated at interval 5 if its performance at interval 5 is in the lowest 20% of performance of all runs at interval 5.

  Incorrect Answers:

  Median:

  Median stopping is an early termination policy based on running averages of primary metrics reported by the runs. This policy computes running averages across all training runs and terminates runs whose performance is worse than the

  median of the running averages.

  Slack:

  Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing

  training run.

  References:

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

• Question 23:

  You use Azure Machine Learning to train a model.

  You must use a sampling method for tuning hyperparameters. The sampling method must pick samples based on how the model performed with previous samples.

  You need to select a sampling method.

  Which sampling method should you use?

  A. Grid

  B. Bayesian

  C. Random

  Correct Answer: B

• Question 24:

  You have the following code. The code prepares an experiment to run a script:

  

  The experiment must be run on local computer using the default environment.

  You need to add code to start the experiment and run the script.

  Which code segment should you use?

  A. run = script_experiment.start_logging()

  B. run = Run(experiment=script_experiment)

  C. ws.get_run(run_id=experiment.id)

  D. run = script_experiment.submit(config=script_config)

  Correct Answer: D

  The experiment class submit method submits an experiment and return the active created run.

  Syntax: submit(config, tags=None, **kwargs)

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment

• Question 25:

  You have the following Azure subscriptions and Azure Machine Learning service workspaces:

  

  You need to obtain a reference to the ml-project workspace. Solution: Run the following Python code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

• Question 26:

  You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.

  You must update the content of the downstream data source of pipeline1 and run the pipeline again

  You need to ensure the new run of pipeline1 fully processes the updated content.

  Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

• Question 27:

  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. allow.reuse

  B. hash_path

  C. data-as_input(name-..)

  D. version

  Correct Answer: A

• Question 28:

  You are developing a machine learning model.

  You must inference the machine learning model for testing.

  You need to use a minimal cost compute target

  Which two compute targets should you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point

  A. Local web service

  B. Remote VM

  C. Azure Databricks

  D. Azure Machine Learning Kubernetes

  E. Azure Container Instances

  Correct Answer: AE

• Question 29:

  You are profiling mltabte data assets by using Azure Machine Learning studio. You need to detect columns with odd or missing values. Which statistic should you analyze?

  A. Profile

  B. Std deviation

  C. Error count

  D. Type

  Correct Answer: C

• Question 30:

  You train and register an Azure Machine Learning model

  You plan to deploy the model to an online endpoint

  You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

  Solution: Create a managed online endpoint and set the value of its auth.mode parameter to aml.token.

  Deploy the model to the online endpoint.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B