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Microsoft DP-100 Exam Questions
Designing and Implementing a Data Science Solution on Azure (Page 8 )

Updated On: 14-Mar-2026
Page 8 of 102
PDF with 504 Questions

You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.
Which of the following is an algorithm that reduces the variances between actual and predicted values?

  1. Fast Forest Quantile Regression
  2. Poisson Regression
  3. Boosted Decision Tree Regression
  4. Linear Regression

Answer(s): D

Explanation:

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/algorithm-module-reference/boosted-decision-tree-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Convolutional Neural Networks (CNNs).
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): B



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Generative Adversarial Networks (GANs).
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): B



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Recurrent Neural Networks (RNNs).
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): A

Explanation:

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



DRAG DROP (Drag and Drop is not supported)
You have been tasked with evaluating the performance of a binary classification model that you created.
You need to choose evaluation metrics to achieve your goal.
Which of the following are the metrics you would choose? Answer by dragging the correct options from the list to the answer area.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



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



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