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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model development, training, and evaluation - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms |
| Topic 2: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 3: Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
| Topic 4: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
1. You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
A) Accumulated Local Effects
B) Feature Dependence Explanations
C) Local Interpretable Model-Agnostic Explanations
D) Feature Permutation Importance Explanations
2. 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) cramersv_plot()
B) correlation_ratio_plot()
C) corr()
D) pearson_plot()
3. Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
A) Data Modeling
B) Data Access
C) Data Monitoring
D) Data Exploration
4. You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
A) Detailed
B) Pass a dictionary that defines a search space
C) ADSTuner doesn’t need a search space to tune the hyperparameters
D) Perfunctory
5. The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
A) oracle_automl.visualize_adaptive_sampling_trials()
B) oracle_automl.visualize_algorithm_selection_trials()
C) oracle_automl.visualize_tuning_trials()
D) oracle_automl.print_trials()
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: D |
