Detailed Answer in Step-by-Step Solution:
* Objective: Define the role of score.py in OCI model artifacts.
* Understand Artifacts: score.py is key for deployment runtime.
* Evaluate Options:
* A: Infra config-Handled by OCI settings, not score.py.
* B: Inference logic-Correct; runs load_model(), predict().
* C: Scaling-Set in deployment, not score.py.
* D: Dependencies-In runtime.yaml, not score.py.
* Reasoning: B aligns with score.py's execution role.
* Conclusion: B is correct.
OCI documentation states: "score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint." A, C, and D are managed elsewhere-only B matches OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

Detailed Answer in Step-by-Step Solution:
* Objective: Publish a conda env to Object Storage.
* Process: Initialize bucket config, then publish env.
* Evaluate Options:
* A: Publishes env with slug-correct final step.
* B: Lists envs-unrelated to publishing.
* C: Sets bucket details-required setup-correct.
* D: Creates env-not publishing.
* E: Activates env-not for sharing.
* Reasoning: C sets up, A executes-standard workflow.
* Conclusion: A and C are correct.
OCI documentation states: "To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage." B, D, and E serve other purposes-only A and C are required per OCI's process.
Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".

Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

Detailed Answer in Step-by-Step Solution:
* Objective: Identify protection for data at rest/transit with customer control.
* Evaluate Options:
* A: Controls-Broad, not specific to encryption.
* B: Isolation-Separates tenants, not crypto-focused.
* C: Encryption-Secures data, allows key management-correct.
* D: Federation-Auth sharing, not data protection.
* Reasoning: C provides crypto control (e.g., Vault keys).
* Conclusion: C is correct.
OCI documentation states: "Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs." A and B are broader, D is unrelated-only C fits per OCI's security model.
Oracle Cloud Infrastructure Security Documentation, "Data Encryption".

Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".