Free Oracle 1z0-1110-25 Practice Exam with Questions & Answers | Set: 2

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:

Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.

Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.

Evaluate Options:

A: False—Overrides can occur post-creation, before running.

B: False—Any authorized user, not just admins, can override defaults.

C: True—Settings can be adjusted before execution starts.

D: False—Defaults can be changed post-creation, pre-execution.

Conclusion: C is correct as it reflects the flexibility of pipeline configuration.

OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B). (Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").

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 common data science techniques.

Define Techniques:

Classification: Predicts categories (e.g., spam vs. not).

Regression: Predicts continuous values (e.g., sales).

Clustering: Groups data (e.g., customer segments).

Evaluate Options:

A, B, C: All are standard ML/statistical methods.

D: Encompasses all—correct as they’re widely used.

Reasoning: These are foundational in data science workflows.

Conclusion: D is correct.

OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).

Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".

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: Identify a widely accepted maxim about data scientists’ time allocation.

Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.

Evaluate Options:

A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.

B: 80% analyzing, 20% finding/preparing—Inverts the common perception.

C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.

Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.

Conclusion: A is correct.

OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.

Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".

Detailed Answer in Step-by-Step Solution:

Objective: Scale up notebook without losing work.

Evaluate Options:

A: Object Storage—Extra steps, inefficient.

B: Block volume—Persists data, seamless scale—correct.

C: Local machine—Risky, cumbersome.

D: Recreate—Loses work, impractical.

Reasoning: B uses OCI’s built-in persistence.

Conclusion: B is correct.

OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Classify a PagerDuty notification in OCI Monitoring.

Understand OCI Monitoring: Involves events, rules, and actions for notifications.

Evaluate Options:

A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.

B: Rule—defines conditions for triggering actions—precedes the action.

C: Function—serverless code, not directly tied to notifications.

D: Event—state change triggering a rule, not the notification itself.

Reasoning: The notification is the action taken after an event/rule—fits A.

Conclusion: A is correct.

OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.

Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Enable credential-less API calls from an instance.

Understand Resource Principal: Allows instances to authenticate via IAM without user creds.

Evaluate Options:

A: Dynamic group + policy—Correct; groups instance, grants access.

B: Dynamic group only—Incomplete; needs policy.

C: User group—Irrelevant for instances.

D: No config—False; setup required.

Reasoning: A sets up resource principal fully—group and perms.

Conclusion: A is correct.

OCI documentation states: “To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services—enables resource principal.” B lacks policy, C is user-based, D is false—only A completes the process per OCI’s IAM setup.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".

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".