Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 321:

  You are building a recurrent neural network to perform a binary classification.

  You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.

  You need to analyze model performance.

  You need to identify whether the classification model is overfitted.

  Which of the following is correct?

  A. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

  B. The training loss decreases while the validation loss increases when training the model.

  C. The training loss stays constant and the validation loss decreases when training the model.

  D. The training loss increases while the validation loss decreases when training the model.

  Correct Answer: B

  An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.

  Reference: https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

• Question 322:

  You create a binary classification model by using Azure Machine Learning Studio.

  You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:

  1.

  iterate all possible combinations of hyperparameters

  2.

  minimize computing resources required to perform the sweep

  You need to perform a parameter sweep of the model.

  Which parameter sweep mode should you use?

  A. Random sweep

  B. Sweep clustering

  C. Entire grid

  D. Random grid

  Correct Answer: D

  Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.

  If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.

  Incorrect Answers:

  B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.

  C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter

  settings might be and want to try all possible combination of values.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model- hyperparameters

• Question 323:

  You create a binary classification model.

  You need to evaluate the model performance.

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

  NOTE: Each correct selection is worth one point.

  A. relative absolute error

  B. precision

  C. accuracy

  D. mean absolute error

  E. coefficient of determination

  Correct Answer: BC

  The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.

  Note: A very natural question is: `Out of the individuals whom the model, how many were classified correctly (TP)?'

  This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

• Question 324:

  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 configure the Tune Model Hyperparameters module.

  Which two values 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.

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

• Question 325:

  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 creating a model to predict the price of a student's artwork depending on the following variables:

  the student's length of education, degree type, and art form.

  You start by creating a linear regression model.

  You need to evaluate the linear regression model.

  Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score, and AUC.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

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

• Question 326:

  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 creating a model to predict the price of a student's artwork depending on the following variables:

  the student's length of education, degree type, and art form.

  You start by creating a linear regression model.

  You need to evaluate the linear regression model.

  Solution: Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Relative Squared Error, Coefficient of Determination are good metrics to evaluate the linear regression model, but the others are metrics for classification models.

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

• Question 327:

  You are a data scientist creating a linear regression model.

  You need to determine how closely the data fits the regression line.

  Which metric should you review?

  A. Root Mean Square Error

  B. Coefficient of determination

  C. Recall

  D. Precision

  E. Mean absolute error

  Correct Answer: B

  Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be

  used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

  Incorrect Answers:

  A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

  C: Recall is the fraction of all correct results returned by the model.

  D: Precision is the proportion of true results over all positive results.

  E: Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

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

• Question 328:

  You are building a machine learning model for translating English language textual content into French language textual content.

  You need to build and train the machine learning model to learn the sequence of the textual content.

  Which type of neural network should you use?

  A. Multilayer Perceptions (MLPs)

  B. Convolutional Neural Networks (CNNs)

  C. Recurrent Neural Networks (RNNs)

  D. Generative Adversarial Networks (GANs)

  Correct Answer: C

  To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).

  Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.

  Reference: https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

• Question 329:

  You are creating a binary classification by using a two-class logistic regression model.

  You need to evaluate the model results for imbalance.

  Which evaluation metric should you use?

  A. Relative Absolute Error

  B. AUC Curve

  C. Mean Absolute Error

  D. Relative Squared Error

  E. Accuracy

  F. Root Mean Square Error

  Correct Answer: B

  One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the

  classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random

  guessing.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating- a-binary-classification-model

• Question 330:

  You write a Python script that processes data in a comma-separated values (CSV) file.

  You plan to run this script as an Azure Machine Learning experiment.

  The script loads the data and determines the number of rows it contains using the following code:

  

  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.upload_file(`row_count', `./data.csv')

  B. run.log(`row_count', rows)

  C. run.tag(`row_count', rows)

  D. run.log_table(`row_count', rows)

  E. run.log_row(`row_count', rows)

  Correct Answer: B

  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)

  Incorrect Answers:

  E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.

  Example: run.log_row("Y over X", x=1, y=0.4)

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