Free Oracle 1z0-1110-25 Practice Exam with Questions & Answers | Set: 3

Detailed Answer in Step-by-Step Solution:

Objective: Match a cache rule criterion for exact URL path and query.

Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.

Evaluate Options:

A: Contains—Partial match, not exact.

B: Is—Exact match of full URL (path + query)—correct.

C: Ends with—Matches end, not full URL.

D: Starts with—Matches start, not full URL.

Reasoning: “URL_IS” checks exact equality—fits requirement.

Conclusion: B is correct.

OCI documentation states: “The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value.” A, C, and D are partial matches—only B ensures identical concatenation per OCI’s caching config.

Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about ML models.

Evaluate Options:

A: True—Data drift (changes in data distribution) degrades performance over time.

B: False—Static predictions don’t improve without retraining.

C: False—Models need updates as data changes, unlike static software.

D: False—Even high-quality models require retraining with new data.

Reasoning: A reflects the reality of data drift, a common ML challenge.

Conclusion: A is correct.

OCI documentation notes: “Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.” B, C, and D contradict this—static predictions don’t improve (B), models aren’t static (C), and retraining is needed (D). A is the accurate aspect.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".

Detailed Answer in Step-by-Step Solution:

Analyze Git Operations: Identify which depend on internet speed.

Evaluate Options:

A. Staging (git add): Local operation—adds files to the index; no network involved.

B. Updating local repo (git pull): Downloads remote changes—requires internet, slowed by poor connectivity.

C. Pushing changes (git push): Uploads local commits to remote—network-dependent, delayed by slow speed.

D. Committing (git commit): Local snapshot—no network needed.

E. Converting to Git repo (git init): Local initialization—no internet required.

Reasoning: Only B and C involve network transfers, directly impacted by slow internet.

Conclusion: B and C are the correct choices.

Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as “bandwidth-sensitive” in OCI’s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user’s machine, unaffected by internet speed. This matches standard Git behavior and OCI’s implementation.

Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

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".

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: Prevent deployment failure due to high utilization.

Evaluate Options:

A: More instances—Scales capacity—correct.

B: Delete—Stops service, not a solution.

C: Fewer instances—Worsens utilization.

D: Larger VM—Increases resource capacity—correct.

E: Reduce bandwidth—Limits load—correct.

Reasoning: A and D boost capacity, E controls demand—proactive fixes.

Conclusion: A, D, E are correct.

OCI documentation advises: “To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume.” B stops service, C reduces capacity—only A, D, E prevent failure per OCI’s scaling options.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".

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: Efficiently vary hyperparameters in OCI Jobs.

Evaluate Options:

A: New job per run—Wastes setup time.

B: Code changes per job—Inefficient, error-prone.

C: Flexible params per run—Efficient, reusable—correct.

D: New job per run—Redundant effort.

Reasoning: C minimizes job creation, maximizes flexibility.

Conclusion: C is correct.

OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.

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

Detailed Answer in Step-by-Step Solution:

Objective: Select an OCI AI service for text and table extraction from documents.

Evaluate Options:

A: Language—Text analysis, not extraction—incorrect.

B: Digital Assistant—Chatbots, not document tasks—incorrect.

C: Speech—Audio transcription, not documents—incorrect.

D: Vision—OCR for text/tables—correct.

Reasoning: Vision’s OCR extracts text and tables from document images.

Conclusion: D is correct.

OCI documentation states: “OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks.” A analyzes text post-extraction, B and C are unrelated—only D fits per OCI’s AI services.

Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".

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".