Oracle 1Z0-1110-26 Exam : Oracle Cloud Infrastructure Data Science Professional

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

1. Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?

A) Identity Federation
B) Security controls
C) Data encryption
D) Customer isolation

2. Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?

A) Validate the model using recent data
B) Redeploy the model
C) Drift monitoring
D) Retrain the model
E) Operational monitoring

3. What is feature engineering in machine learning used for?

A) To help understand the dataset features
B) To perform parameter tuning
C) To transform existing features into new ones
D) To interpret ML models

4. You want to make your model more parsimonious to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

A) corr()
B) pearson_plot()
C) correlation_ratio_plot()
D) cramersv_plot()

5. A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?

A) Data Access, Feature Exploration, Feature Engineering, Data Exploration, Modeling
B) Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling
C) Data Access, Feature Exploration, Data Exploration, Feature Engineering, Modeling
D) Data Access, Data Exploration, Feature Exploration, Feature Engineering, Modeling

Solutions: