Oracle 1z0-1110-25 Exam With Confidence Using Practice Dumps

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about OCI Jobs logging.

Understand Logging: Jobs can log stdout/stderr to OCI Logging service.

Evaluate Options:

A: False—Each run has its own log, not a single job log.

B: False—Logging is optional, not mandatory.

C: True—When enabled, stdout/stderr are auto-captured.

D: False—Logs persist unless explicitly deleted.

Reasoning: C matches OCI’s automatic logging feature.

Conclusion: C is correct.

OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a widely accepted maxim about data scientists’ time allocation.

Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.

Evaluate Options:

A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.

B: 80% analyzing, 20% finding/preparing—Inverts the common perception.

C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.

Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.

Conclusion: A is correct.

OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.

Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".

Detailed Answer in Step-by-Step Solution:

Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.

Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.

Evaluate Options:

A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.

B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.

C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).

D. Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.

Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.

Conclusion: D is the best approach for detailed investigation.

In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.