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 OCI Data Science interaction method that doesn't require scripting.
* Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services- some are GUI-based, others script-based.
* Evaluate Options:
* A. OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click-no scripting needed.
* B. CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
* C. Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
* D. REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
* Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
* Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as "a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts." In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console's GUI distinguishes it as the only option functioning without scripting, aligning with Oracle's design for accessibility to non-programmers.
Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.

Detailed Answer in Step-by-Step Solution:
* Objective: Choose the better model for cancer prediction based on metrics.
* Understand Metrics:
* Accuracy: Overall correct predictions.
* Recall: True positives / (True positives + False negatives)-crucial for cancer (minimizing misses).
* Context: Cancer prediction prioritizes recall-false negatives (missed cancers) are critical.
* Evaluate Models:
* Model 1: 80% accuracy, 70% recall-Misses more cancers.
* Model 2: 75% accuracy, 85% recall-Misses fewer cancers.
* Evaluate Options:
* A: High recall-True, but lacks context.
* B: High accuracy-Misses recall's importance.
* C: Recall's impact-Correct for cancer use case-best.
* D: Lesser recall impact-Incorrect for this priority.
* Reasoning: C emphasizes recall's critical role-aligns with medical needs.
* Conclusion: C is correct.
OCI documentation advises: "For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at
85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C)." A is partial, B overlooks context, D reverses priority-only C fits OCI's ML evaluation guidance for this scenario.
Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

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: Get all AutoML trial results.
* Understand AutoML: Trials include hyperparameter tuning outcomes.
* Evaluate Options:
* A: print_trials()-Displays all trial results-correct.
* B: visualize_tuning_trials()-Visualizes tuning, not full list.
* C: visualize_adaptive_sampling_trials()-Specific to sampling, not all trials.
* D: visualize_algorithm_selection_trials()-Specific to algorithms, not all.
* Reasoning: A provides comprehensive trial output.
* Conclusion: A is correct.
OCI AutoML documentation states: "print_trials() outputs a table of all trials performed, including hyperparameters and scores." Visualization methods (B, C, D) focus on specific aspects-only A gives the full list.
Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".