Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 31:

  You are evaluating a completed binary classification machine learning model.

  You need to use the precision as the valuation metric.

  Which visualization should you use?

  A. Binary classification confusion matrix

  B. box plot

  C. Gradient descent

  D. coefficient of determination

  Correct Answer: A

  https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine- learning/

• Question 32:

  You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

  You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

  Which two hyperparameters should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Number of hidden nodes

  B. Learning Rate

  C. The type of the normalizer

  D. Number of learning iterations

  E. Hidden layer specification

  Correct Answer: DE

  D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.

  E: For Hidden layer specification, select the type of network architecture to create. Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden

  layers.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two- class-neural- network

• Question 33:

  You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training

  script that runs on a compute cluster with 20 nodes.

  The code implements Bandit termination policy with slack_factor set to 02 and a sweep job with max_concurrent_trials set to 10.

  You must increase effectiveness of the tuning process by improving sampling convergence.

  You need to select which sampling convergence to use.

  What should you select?

  A. Set the value of slack.factor of earty.termination policy to 0.1.

  B. Set the value of max_concurrent_trials to 4.

  C. Set the value of slack_factor of eartyjermination policy to 0.9.

  D. Set the value of max.concurrentjrials to 20.

  Correct Answer: C

• Question 34:

  You have an Azure Machine Learning workspace. You build a deep learning model.

  You need to publish a GPU-enabled model as a web service.

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

  NOTE: Each correct selection is worth one point.

  A. Azure Kubernetes Service (AKS)

  B. Azure Container Instances (ACI)

  C. Local web service

  D. Azure Machine Learning compute clusters

  Correct Answer: AB

• Question 35:

  HOTSPOT

  You need to build a feature extraction strategy for the local models.

  How should you complete the code segment? To answer, select the appropriate options in the answer area;

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

• Question 36:

  You need to use the Python language to build a sampling strategy for the global penalty detection models. How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: import pytorch as deeplearninglib

  Box 2: ..DistributedSampler(Sampler)..

  DistributedSampler(Sampler):

  Sampler that restricts data loading to a subset of the dataset.

  It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive

  to it.

  Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.

  Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10)

  Incorrect Answers: ..SGD..

  Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow.

  Box 4: .. nn.parallel.DistributedDataParallel.. DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.

  References: https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py

• Question 37:

  HOTSPOT

  You need to identify the methods for dividing the data according to the testing requirements.

  Which properties should you select? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Scenario: Testing

  You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

  Box 1: Assign to folds

  Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

  Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.

  Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

  Box 2: Partition evenly

  Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:

  Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample

• Question 38:

  You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.

  How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Mutual Information.

  The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.

  Box 2: MedianValue

  MedianValue is the feature column, , it is the predictor of the dataset.

  Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

• Question 39:

  You need to replace the missing data in the AccessibilityToHighway columns.

  How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Replace using MICE

  Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by

  Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

  Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing

  values.

  Box 2: Propagate

  Cols with all missing values indicate if columns of all missing values should be preserved in the output.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

• Question 40:

  HOTSPOT

  You need to configure the Edit Metadata module so that the structure of the datasets match.

  Which configuration options should you select? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Floating point

  Need floating point for Median values.

  Scenario: An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the

  MedianValue in numerical format.

  Box 2: Unchanged Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories. For example, you might have a column that contains the numbers 0,1 and 2, but know that the numbers actually mean "Smoker", "Non smoker" and "Unknown". In that case, by flagging the column as categorical you can ensure that the

  values are not used in numeric calculations, only to group data.