Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 291:

  You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.

  Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.

  You need to produce the distribution.

  Which type of distribution should you produce?

  A. Unpaired t-test with a two-tail option

  B. Unpaired t-test with a one-tail option

  C. Paired t-test with a one-tail option

  D. Paired t-test with a two-tail option

  Correct Answer: D

  Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.

  Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

  

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t- test

  https://en.wikipedia.org/wiki/Student%27s_t-test

• Question 292:

  You are building a regression model for estimating the number of calls during an event.

  You need to determine whether the feature values achieve the conditions to build a Poisson regression model.

  Which two conditions must the feature set contain? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. The label data must be a negative value.

  B. The label data must be whole numbers.

  C. The label data must be non-discrete.

  D. The label data must be a positive value.

  E. The label data can be positive or negative.

  Correct Answer: BD

  Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:

  1.

  The response variable has a Poisson distribution.

  2.

  Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.

  3.

  A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression

• Question 293:

  You are performing feature engineering on a dataset.

  You must add a feature named CityName and populate the column value with the text London.

  You need to add the new feature to the dataset.

  Which Azure Machine Learning Studio module should you use?

  A. Edit Metadata

  B. Filter Based Feature Selection

  C. Execute Python Script

  D. Latent Dirichlet Allocation

  Correct Answer: A

  Typical metadata changes might include marking columns as features.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata

• Question 294:

  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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

  You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

  You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  There is a missing line: conda_packages=['scikit-learn'], which is needed.

  Correct example:

  sk_est = Estimator(source_directory='./my-sklearn-proj', script_params=script_params,

  compute_target=compute_target,

  entry_script='train.py',

  conda_packages=['scikit-learn'])

  Note:

  The Estimator class represents a generic estimator to train data using any supplied framework.

  This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for Chainer, PyTorch, TensorFlow, and SKLearn.

  Example:

  from azureml.train.estimator import Estimator

  script_params = {

  # to mount files referenced by mnist dataset

  '--data-folder': ds.as_named_input('mnist').as_mount(),

  '--regularization': 0.8

  }

  Reference:

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

• Question 295:

  You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.

  The experiment fails.

  You need to troubleshoot the failed experiment.

  What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

  A. Use the get_metrics() method of the run object to retrieve the experiment run logs.

  B. Use the get_details_with_logs() method of the run object to display the experiment run logs.

  C. View the log files for the experiment run in the experiment folder.

  D. View the logs for the experiment run in Azure Machine Learning studio.

  E. Use the get_output() method of the run object to retrieve the experiment run logs.

  Correct Answer: BD

  Use get_details_with_logs() to fetch the run details and logs created by the run.

  You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.

  Incorrect Answers:

  A: You can view the metrics of a trained model using run.get_metrics().

  E: get_output() gets the output of the step as PipelineData.

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs

• Question 296:

  You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.

  You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction

  data input.

  You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.

  What should the entry script do?

  A. Register the model with appropriate tags and properties.

  B. Create a Conda environment for the web service compute and install the necessary Python packages.

  C. Load the model and use it to predict labels from input data.

  D. Start a node on the inference cluster where the web service is deployed.

  E. Specify the number of cores and the amount of memory required for the inference compute.

  Correct Answer: C

  The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data

  that the model expects and returns.

  The two things you need to accomplish in your entry script are:

  Loading your model (using a function called init())

  Running your model on input data (using a function called run())

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

• Question 297:

  You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.

  Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days.

  You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand.

  Which deployment compute option should you use?

  A. attached Azure Databricks cluster

  B. Azure Container Instance (ACI)

  C. Azure Kubernetes Service (AKS) inference cluster

  D. Azure Machine Learning Compute Instance

  E. attached virtual machine in a different region

  Correct Answer: C

  Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network.

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

• Question 298:

  You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameter.

  In previous model training and tuning runs, many models showed similar performance.

  You need to select an early termination policy that meets the following requirements:

  1.

  accounts for the performance of all previous runs when evaluating the current run

  2.

  avoids comparing the current run with only the best performing run to date

  Which two early termination policies should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Median stopping

  B. Bandit

  C. Default

  D. Truncation selection

  Correct Answer: AD

  The Median Stopping policy computes running averages across all runs and cancels runs whose best performance is worse than the median of the running averages.

  If no policy is specified, the hyperparameter tuning service will let all training runs execute to completion.

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.medianstoppingpolicy https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.truncationselectionpolicy https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy

• Question 299:

  You plan to use the Hyperdrive feature of Azue 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.

  1.

  learning_rate: any value between 0.001 and 0.1

  2.

  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. No sampling

  B. Grid sampling

  C. Bayesian sampling

  D. Random sampling

  Correct Answer: CD

  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)

  }

  )

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

• Question 300:

  You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.

  The model will be retrained each month as new data is available.

  You must register the model for use in a batch inference pipeline.

  You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.

  What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Specify a different name for the model each time you register it.

  B. Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline.

  C. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

  D. Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.

  E. Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.

  Correct Answer: CE

  E: Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric.

  Reference:

  https://notebooks.azure.com/xavierheriat/projects/azureml-getting-started/html/how-to-use-azureml/ deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb