Oracle 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional

Detailed Answer in Step-by-Step Solution:

Objective: Identify the architecture with “never trust, always verify.”

Evaluate Options:

A: Federated identity—Shares auth, not verification-focused.

B: Zero trust—Explicitly “never trust, always verify”—correct.

C: Fluid perimeter—Adaptive, not the core principle.

D: Defense in depth—Layered, not verification-centric.

Reasoning: Zero trust matches the stated principle exactly.

Conclusion: B is correct.

OCI documentation states: “Zero trust (B) architecture operates on ‘never trust, always verify,’ requiring continuous authentication and authorization.” A, C, and D have different focuses—only B aligns with OCI’s security philosophy.

Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".

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: Identify an open model format for cross-platform ML model execution.

Evaluate Options:

A. PySpark: A big data framework, not a model format.

B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.

C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.

D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.

Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.

Conclusion: D is the correct choice.

ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

Detailed Answer in Step-by-Step Solution:

Objective: Identify supported annotation classes in OCI Data Labeling.

Understand Tool: Supports image/text annotations for ML.

Evaluate Options:

A: Object detection—Yes (bounding boxes).

B: Named entity—Text-specific, not primary for images.

C: Classification—Yes (labels for images/text).

D: Key-point—Not listed in OCI docs.

E: Polygonal—Not explicitly supported.

F: Semantic segmentation—Yes (pixel-level).

Reasoning: A, C, F match OCI’s image/text focus.

Conclusion: A, C, F are correct.

OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.

Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Optimize embarrassingly parallel workload in OCI Jobs.

Evaluate Options:

A: Sequential runs—Inefficient for parallel tasks.

B: Simultaneous runs—Maximizes parallelism—correct.

C: False—Jobs support parallelism.

D: One job per run—Misstates capability, wasteful.

Reasoning: B leverages OCI’s parallel run support.

Conclusion: B is correct.

OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.

Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".

Detailed Answer in Step-by-Step Solution:

Understand ORM Template: It automates OCI Data Science setup with predefined configurations.

Evaluate Components:

A: User groups are created for role-based access—automated.

B: Dynamic groups (e.g., for notebook sessions) are included—automated.

C: Individual users require manual creation via IAM—not automated.

D: Basic policies (e.g., access to Data Science resources) are included—automated.

Reasoning: ORM focuses on infrastructure and permissions, not user accounts.

Conclusion: C is the exception.

The OCI Resource Manager template for Data Science “automatically provisions user groups, dynamic groups, and policies for basic use cases,” but “individual users must be created separately in IAM and assigned to groups.” C is the only item not handled by the template, per the documentation.

Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".

Detailed Answer in Step-by-Step Solution:

Objective: Find optimal compute shape balancing cost and time.

Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).

Evaluate Options:

A: Tuning parameters when underutilized—focuses on model, not shape optimization.

B: Strongest shape—Costly, ignores balance; overkill likely.

C: Scale up from small shape when fully utilized—Balances cost/time effectively.

D: Random start with pre-tests—Unsystematic and inefficient.

Reasoning: C incrementally increases resources based on utilization, optimizing both factors.

Conclusion: C is correct.

OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus—balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure—only C aligns with this method.

Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three stages in OCI AutoML Pipeline.

Understand Pipeline: Automates ML steps from data to model training.

Evaluate Options:

A: Feature Selection—Selects relevant features—correct.

B: Adaptive Sampling—Reduces data size—correct.

C: Model Deployment—Post-pipeline step—incorrect.

D: Feature Extraction—Not explicit in OCI AutoML—incorrect.

E: Algorithm Selection—Chooses best model—correct.

Reasoning: A, B, E are core automated stages; C and D are separate.

Conclusion: A, B, E are correct.

OCI documentation lists “AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning.” Deployment (C) is post-pipeline, and extraction (D) isn’t highlighted—only A, B, E are included per OCI’s design.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

Detailed Answer in Step-by-Step Solution:

Objective: Understand parallel execution in OCI Pipelines.

Evaluate Options:

A: False—Pipelines support parallelism.

B: True—DAG allows sequential/parallel steps—correct.

C: False—Not all steps must be parallel.

D: False—Independence enables parallelism.

Reasoning: B reflects OCI’s DAG-based flexibility.

Conclusion: B is correct.

OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".

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".