Pass the Oracle Oracle Cloud Solutions Infrastructure 1z0-1110-25 Questions and answers with CertsForce

Detailed Answer in Step-by-Step Solution:

Objective: Predict AutoML’s output when time runs out during tuning.

Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.

Evaluate Options:

A: Best-known config—Logical, reflects optimization goal—correct.

B: Last config—Ignores prior better results—incorrect.

C: Minimum learning rate—Arbitrary, not performance-based.

D: Random—Defeats tuning purpose.

Reasoning: AutoML prioritizes the best config found within the budget.

Conclusion: A is correct.

OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.

Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

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:

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: Securely authenticate a recurring OCI Job.

Evaluate Options:

A: PAR—Limited duration, insecure for recurring jobs.

B: Resource principal—Secure, managed auth for Jobs—correct.

C: Personal config—Unscalable, security risk.

D: Vault with personal keys—Complex, still uses user creds.

Reasoning: B uses OCI’s native, secure resource principal mechanism.

Conclusion: B is correct.

OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.

Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

Detailed Answer in Step-by-Step Solution:

Objective: Identify export formats for OCI Data Labeling.

Understand Export: Annotated data is exported for ML use.

Evaluate Options:

A: CONLL V2003—Text-specific, not OCI-supported.

B: COCO—Standard for image annotations—correct.

C: Proprietary JSON—OCI’s default format—correct.

D: Spacy—NLP format, not supported.

Reasoning: B and C are image-compatible per OCI docs.

Conclusion: B and C are correct.

OCI documentation states: “Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service.” CONLL (A) and Spacy (D) are text/NLP-focused—not supported for OCI’s image annotations.

Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".

Detailed Answer in Step-by-Step Solution:

Objective: Run a complex Python project as an OCI Job.

Evaluate Options:

A: Auto-identification—False; entrypoint must be set.

B: Rewrite—Unnecessary, inefficient.

C: Auto-executable—False; needs explicit entrypoint.

D: ZIP with entrypoint—Correct, flexible approach.

Reasoning: D preserves structure, specifies execution.

Conclusion: D is correct.

OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.

Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

Detailed Answer in Step-by-Step Solution:

Objective: Assess if bias is a common issue in data science.

Define Bias: Systematic errors in data/models (e.g., skewed training data).

Evaluate Statement:

Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.

Examples: Gender bias in hiring models, racial bias in facial recognition.

Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.

Conclusion: A (True) is correct.

OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.

Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

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: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Notebook Sessions: Run in a managed environment with specific permissions.

Evaluate Options:

A: False—No root privileges; users operate as datascience with limited sudo.

B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).

C: False—Yum isn’t available; pip is the primary tool as a normal user.

D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).

E: True—Custom repos are supported with proper network config.

Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.

Conclusion: B, D (corrected), E are correct.

OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".

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".