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Quiz Newest Oracle - 1z0-1110-25 - Latest Oracle Cloud Infrastructure 2025 Data Science Professional Practice Questions

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q62-Q67):

NEW QUESTION # 62
Where are OCI secrets stored?

  • A. OCI Object Storage
  • B. Autonomous Data Warehouse
  • C. Oracle Databases
  • D. OCI Vault

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.
* Evaluate Options:
* A: Object Storage is for general data, not secure secret management.
* B: Vault is OCI's service for storing and managing secrets securely.
* C: Autonomous Data Warehouse is for analytics, not secret storage.
* D: Oracle Databases store data, not OCI-specific secrets.
* Reasoning: Vault is purpose-built for secrets with encryption and access control.
* Conclusion: B is correct.
OCI Vault "provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies." Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes-only Vault (B) is designed for secrets per OCI's security architecture.
Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".


NEW QUESTION # 63
Where do calls to stdout and stderr from score.py go in a model deployment?

  • A. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
  • B. The OCI Cloud Shell, which can be accessed from the console
  • C. The OCI console
  • D. The file that was defined for them on the Virtual Machine (VM)

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate score.py output in OCI model deployment.
* Understand Deployment: Logs are centralized in OCI Logging.
* Evaluate Options:
* A: VM file-Not default; requires custom config-incorrect.
* B: Predict log in OCI Logging-Standard destination-correct.
* C: Cloud Shell-Separate tool, not logs-incorrect.
* D: Console-UI, not raw logs-incorrect.
* Reasoning: B aligns with OCI's logging integration.
* Conclusion: B is correct.
OCI documentation states: "score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment." A isn't standard, C and D don't receive logs-only B fits OCI's logging setup.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".


NEW QUESTION # 64
Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud Infrastructure (OCI) Data Science developed and added to the notebook session experience?

  • A. Terminal
  • B. Environment Explorer
  • C. Command Palette
  • D. Table of Contents
  • E. Notebook Examples

Answer: B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.
* Understand Extensions: OCI enhances JupyterLab with proprietary tools.
* Evaluate Options:
* A: Environment Explorer-OCI-specific, non-open-correct.
* B: Table of Contents-Open-source Jupyter-incorrect.
* C: Command Palette-Open-source Jupyter-incorrect.
* D: Notebook Examples-OCI-specific, non-open-correct.
* E: Terminal-Open-source Jupyter-incorrect.
* Reasoning: A and D are OCI proprietary; others are standard JupyterLab.
* Conclusion: A and D are correct.
OCI documentation states: "OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code-both proprietary enhancements." B, C, and E are open-source JupyterLab defaults-only A and D are OCI-specific per the notebook session design.
Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".


NEW QUESTION # 65
Which statement about resource principals is true?

  • A. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
  • B. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.
  • C. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
  • D. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
* Evaluate Options:
* A: False-Resource principals eliminate manual credential management.
* B: False-They're secure, leveraging IAM policies, not less secure than API keys.
* C: False-Data Science supports resource principals for accessing resources (e.g., Object Storage).
* D: True-Resource principals are an IAM feature authorizing resources as actors.
* Reasoning: D captures the essence of resource principals as an IAM mechanism.
* Conclusion: D is correct.
OCI documentation states: "A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies." This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".


NEW QUESTION # 66
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

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Feature Engineering: It's the process of creating or modifying features to improve model performance.
* Evaluate Options:
* A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
* B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
* C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering-correct.
* D: Understanding features occurs during exploration, not engineering.
* Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of- week), distinct from tuning or interpretation.
* Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as "the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy." Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".


NEW QUESTION # 67
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