Oracle 1Z0-1110-25 Practice Exam Questions & Answers

Detailed Answer in Step-by-Step Solution:

Objective: Locate artifacts for ADS model deployment.

Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.

Evaluate Options:

A: Vault---Stores secrets, not models.

B: Depository---Not an OCI term.

C: Model Catalog---Stores models/artifacts for deployment---correct.

D: Artifactory---Not an OCI service.

Reasoning: Model Catalog is OCI's model repository for ADS.

Conclusion: C is correct.

OCI documentation states: ''ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.'' Vault (A) is for secrets, B and D aren't real---only C supports ADS deployment.

: Oracle Cloud Infrastructure Data Science Documentation, 'ADS Model Deployment'.

Detailed Answer in Step-by-Step Solution:

Objective: Sequence steps for PySpark app development.

Steps:

Launch notebook: First---sets up environment.

Install PySpark conda: Second---adds Spark libraries.

Configure core-site.xml: Third---connects to data.

Develop app: Fourth---writes code.

Data Flow: Fifth---scales (optional).

Evaluate: D (1, 2, 3, 4, 5) matches this logical order.

Reasoning: Notebook first, then setup and coding.

Conclusion: D is correct.

OCI documentation states: ''1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core-site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D).'' Other orders (A, B, C) misplace notebook launch or config---D is correct.

: Oracle Cloud Infrastructure Data Science Documentation, 'PySpark Development'.

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: 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: Visualize correlation between continuous and categorical features.

Evaluate Options:

A: Pearson---Continuous vs. continuous---incorrect.

B: Cramer's V---Categorical vs. categorical---incorrect.

C: Correlation ratio---Continuous vs. categorical---correct.

D: General correlation---Not specific to mixed types.

Reasoning: Correlation ratio handles mixed feature types for heatmaps.

Conclusion: C is correct.

OCI documentation states: ''correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.'' Pearson (A) and Cramer's (B) are type-specific, corr() (D) is broad---only C fits per ADS capabilities.

: Oracle Cloud Infrastructure ADS SDK Documentation, 'Correlation Visualization'.

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: Identify the WAF component that controls request actions based on criteria.

Understand WAF Components:

Protection Rules: Define conditions and actions (e.g., allow, block, log).

Bot Management: Handles bot traffic, not general request rules.

Origin: Backend server endpoint, not rule-based.

WAF Policy: Umbrella config, but rules specify actions.

Evaluate Options:

A: Protection rules---Set specific criteria and actions---correct.

B: Bot Management---Bot-specific, not general requests.

C: Origin---Defines source, not actions.

D: WAF policy---Broad config, not the granular rules.

Reasoning: Protection rules directly manage request behavior---fit the requirement.

Conclusion: A is correct.

OCI documentation states: ''Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.'' Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn't the action specifier---only A aligns with OCI's WAF configuration.

: Oracle Cloud Infrastructure WAF Documentation, 'Protection Rules'.