1z0-1110-25 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

Detailed Answer in Step-by-Step Solution:

Objective: Fix network timeout for pip install in a notebook.

Evaluate Options:

A: FastConnect—On-premises link, not public internet.

B: VNIC—Default, not the issue.

C: NAT Gateway—Grants internet access—correct.

D: Service Gateway—OCI services, not PyPI.

Reasoning: C enables outbound traffic to public repos.

Conclusion: C is correct.

OCI documentation states: “A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI.” A, B, and D don’t provide this—only C resolves the timeout.

Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three data formats supported by ADS SDK for loading data.

Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.

Evaluate Options:

A. DOCX: Not natively supported—requires conversion (e.g., to text).

B. Pandas DataFrame: Supported—core format for data manipulation in ADS.

C. JSON: Supported—common structured data format.

D. Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).

E. XML: Supported—parseable structured format.

Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.

Conclusion: B, C, E are correct.

OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.

Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".

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: Sequence OCI AutoML pipeline stages.

Stages:

Adaptive sampling: Reduces data size if large.

Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type.

Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

Detailed Answer in Step-by-Step Solution:

Objective: Determine truth about WAF origin management.

Understand WAF: Protects apps by routing traffic via origins.

Evaluate Statements:

A: Multiple origins—True; WAF supports this.

B: Single active origin—True; only one is active per policy.

Evaluate Options:

C: B only—False; A is true.

D: Both false—Incorrect.

E: Both true—Correct per OCI WAF.

F: A only—False; B is true.

Conclusion: E is correct.

OCI documentation states: “WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)—both are true (E).” C, D, and F misalign—E matches OCI’s WAF origin management.

Oracle Cloud Infrastructure WAF Documentation, "Origin Management".

Detailed Answer in Step-by-Step Solution:

Objective: Determine minimum log storage duration before archiving in Logging Analytics.

Understand Logging Analytics: Logs are active before archival.

Evaluate Options:

A: 60 days—Too long for minimum.

B: 10 days—Too short.

C: 30 days—Standard minimum—correct.

D: 15 days—Not OCI’s default.

Reasoning: 30 days is OCI’s documented minimum active period.

Conclusion: C is correct.

OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

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: Share a custom conda environment in OCI Data Science.

Understand Commands: OCI provides odsc CLI for environment management.

Evaluate Options:

A: clone duplicates an environment locally—not for sharing.

B: publish uploads the environment to Object Storage for team access—correct.

C: modify doesn’t exist as a standard command.

D: install sets up an environment locally—not for sharing.

Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.

Conclusion: B is the correct command.

The OCI Data Science CLI documentation states: “Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.” clone (A) is for local duplication, modify (C) isn’t valid, and install (D) is for local setup—not sharing. B is the designated sharing mechanism.

Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".

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.

Detailed Answer in Step-by-Step Solution:

Objective: Identify the Data Safe feature that reduces personal data exposure.

Understand Data Safe: Secures sensitive data in OCI databases.

Evaluate Options:

A: Encryption—Protects data, doesn’t minimize it.

B: Assessment—Identifies risks, doesn’t alter data.

C: Masking—Obfuscates personal data (e.g., SSNs)—correct.

D: Discovery—Locates sensitive data, doesn’t reduce it.

E: Auditing—Tracks access, doesn’t minimize data.

Reasoning: Masking replaces sensitive data, reducing risk for teams—fits goal.

Conclusion: C is correct.

OCI documentation states: “Data masking (C) in Data Safe transforms sensitive data into anonymized versions, minimizing exposure for test, dev, and analytics use.” A protects, B assesses, D finds, E audits—only C reduces data per OCI’s Data Safe features.

Oracle Cloud Infrastructure Data Safe Documentation, "Data Masking Overview".