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Free DP-100: Designing and Implementing a Data Science Solution on Azure (beta) Exam Questions and Answers
Last updated: August 15, 2025
Examtopics DP-100: Designing and Implementing a Data Science Solution on Azure (beta) Prepaway Real Exam Questions and dumps free download
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1667-DP-100: Designing and Implementing a Data Science Solution on Azure (beta)
#Question
In a scenario where you have a large amount of unstructured text data, which technique would be appropriate?
A.
Clustering
B.
Data pruning
C.
Sentiment Analysis
D.
Regression
Correct Answer:
C
Sentiment analysis is a technique used to extract insights from unstructured text data, helping to assess emotional context.
#Question
What is the primary purpose of utilizing feature engineering before model training?
A.
Normalize output
B.
Enhance the predictive power
C.
Reduce noise in predictions
D.
Increase data volume
Correct Answer:
B
Feature engineering enhances the predictive power of models by creating meaningful features that can improve model learning.
#Question
How can you ensure your model remains unbiased?
A.
Use larger datasets
B.
Decrease complexity
C.
Remove sensitive features
D.
Monitor for fairness regularly
Correct Answer:
D
Regularly monitoring for fairness in the model helps ensure that it does not discriminate against any group, maintaining unbiased predictions.
#Question
If your machine learning pipeline requires continuous feedback from production, which Azure service would be best?
A.
Azure Machine Learning
B.
Azure DevOps
C.
Azure Logic Apps
D.
Azure Data Lake
Correct Answer:
B
Azure DevOps is ideal for continuous feedback and integration between development and operations for machine learning models in production.
#Question
In Azure ML, what feature allows for the annotation of images and text for model training?
A.
Data collection tools
B.
Labeling tools
C.
Model Evaluation
D.
Integrated notebooks
Correct Answer:
B
The labeling tools in Azure ML enable efficient annotation of images and text data, which is crucial for training supervised models.
#Question
How would you define feature scaling?
A.
Reducing data size
B.
Modifying data values to a common scale
C.
Reducing data variability
D.
Increasing data complexity
Correct Answer:
B
Feature scaling modifies the range of feature values to ensure that each feature contributes equally in models that rely on distances, like k-NN.
#Question
What is the purpose of a confusion matrix in classification?
A.
To explore region overlap
B.
To summarize prediction results
C.
To tune hyperparameters
D.
To visualize data trends
Correct Answer:
B
A confusion matrix summarizes the performance of a classification model, providing insights on true positives, false positives, true negatives, and false negatives.
1667-DP-100: Designing and Implementing a Data Science Solution on Azure (beta)
#Question
In a scenario where you have a large amount of unstructured text data, which technique would be appropriate?
A.
Clustering
B.
Data pruning
C.
Sentiment Analysis
D.
Regression
Correct Answer:
C
Sentiment analysis is a technique used to extract insights from unstructured text data, helping to assess emotional context.
#Question
What is the primary purpose of utilizing feature engineering before model training?
A.
Normalize output
B.
Enhance the predictive power
C.
Reduce noise in predictions
D.
Increase data volume
Correct Answer:
B
Feature engineering enhances the predictive power of models by creating meaningful features that can improve model learning.
#Question
How can you ensure your model remains unbiased?
A.
Use larger datasets
B.
Decrease complexity
C.
Remove sensitive features
D.
Monitor for fairness regularly
Correct Answer:
D
Regularly monitoring for fairness in the model helps ensure that it does not discriminate against any group, maintaining unbiased predictions.
#Question
If your machine learning pipeline requires continuous feedback from production, which Azure service would be best?
A.
Azure Machine Learning
B.
Azure DevOps
C.
Azure Logic Apps
D.
Azure Data Lake
Correct Answer:
B
Azure DevOps is ideal for continuous feedback and integration between development and operations for machine learning models in production.
#Question
In Azure ML, what feature allows for the annotation of images and text for model training?
A.
Data collection tools
B.
Labeling tools
C.
Model Evaluation
D.
Integrated notebooks
Correct Answer:
B
The labeling tools in Azure ML enable efficient annotation of images and text data, which is crucial for training supervised models.
#Question
How would you define feature scaling?
A.
Reducing data size
B.
Modifying data values to a common scale
C.
Reducing data variability
D.
Increasing data complexity
Correct Answer:
B
Feature scaling modifies the range of feature values to ensure that each feature contributes equally in models that rely on distances, like k-NN.
#Question
What is the purpose of a confusion matrix in classification?
A.
To explore region overlap
B.
To summarize prediction results
C.
To tune hyperparameters
D.
To visualize data trends
Correct Answer:
B
A confusion matrix summarizes the performance of a classification model, providing insights on true positives, false positives, true negatives, and false negatives.
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