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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model development, training, and evaluation - Model saving, cataloging, and versioning - Deploy models and manage endpoints |
| Topic 3: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| Topic 4: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
| Topic 5: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
- A. 60 days
- B. 10 days
- C. 15 days
- D. 30 days
Correct Answer: D 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?
- A. auto_transform()
- B. fit_transform()
- C. visualize_transforms()
- D. get_transformed_dataset()
Correct Answer: A 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
You want to make your model more frugal 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 is appropriate to display the comparability between Continuous and Categorical features?
- A. cramersv_plot()
- B. pearson_plot()
- C. corr()
- D. correlation_ratio_plot()
Correct Answer: D 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%
- A. Model 2, because recall has more impact on predictions in this use case
- B. Model 2, because recall is high
- C. Model 1, because the test accuracy is high
- D. Model 1, because recall has lesser impact on predictions in this use case
Correct Answer: A 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
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. Local Interpretable Model-Agnostic Explanations
- B. Feature Dependence Explanations
- C. Accumulated Local Effects
- D. Feature Permutation Importance Explanations
Correct Answer: C 🗳️
Explanation: Only visible for TrainingDumps members. You can sign-up / login (it's free).
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