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Free 70-774: Perform Cloud Data Science with Azure Machine Learning Exam Questions and Answers
Last updated: March 30, 2025
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1607-70-774: Perform Cloud Data Science with Azure Machine Learning
#Question
Why is updating previously trained models important in machine learning projects?
A.
To decrease model speed
B.
To complicate user interfaces
C.
To maintain performance relevance
D.
To reduce input complexity
Correct Answer:
C
Updating previously trained models ensures they remain relevant and maintain performance as data patterns and underlying conditions change over time.
#Question
How can "Hyperparameter Tuning" improve a model's accuracy in Azure ML?
A.
By ignoring default settings
B.
By increasing model complexity
C.
By testing various parameter values
D.
By reducing variables involved
Correct Answer:
C
Hyperparameter tuning enhances model accuracy by systematically testing various parameter values to identify optimal configurations.
#Question
What is the goal of "Feature Normalization" in data preprocessing?
A.
To downscale irrelevant features
B.
To reduce model complexity
C.
To create binary outputs
D.
To keep feature values consistent
Correct Answer:
D
Feature normalization ensures that feature values are consistent and within a certain range, improving the performance of various algorithms.
#Question
When might "Image Recognition" capabilities be vital in Azure ML?
A.
Augmenting customer-service chatbots
B.
Analyzing visual data
C.
Analyzing text data
D.
Processing streaming data
Correct Answer:
B
Image recognition capabilities are vital for analyzing visual data, such as photographs, to derive insights and inform machine learning modeling.
#Question
How does an "XGBoost" algorithm operate in machine learning approaches?
A.
It clusters data
B.
It simplifies regression analysis
C.
It tracks model versions
D.
It enhances decision trees
Correct Answer:
D
XGBoost is an implementation of gradient boosted decision trees that aims to optimize performance and accuracy, particularly in classification tasks.
#Question
What method can be used to improve the performance of a recommendation engine in Azure ML?
A.
Collaborative filtering
B.
Redundant feature generation
C.
Ignoring user interactions
D.
Data duplication
Correct Answer:
A
Collaborative filtering helps enhance the performance of recommendation engines by leveraging user interactions data to suggest relevant items.
#Question
How may "Cluster Analysis" assist in unsupervised learning tasks?
A.
By finding groups in data
B.
By confirming model stability
C.
By creating labeled datasets
D.
By reducing feature variety
Correct Answer:
A
Cluster analysis identifies patterns and natural groupings within data, providing insights in unsupervised learning tasks.
1607-70-774: Perform Cloud Data Science with Azure Machine Learning
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