Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".

Detailed Answer in Step-by-Step Solution:
* Objective: Identify correct statements about installing Python libraries in OCI Data Science.
* Understand Environment: Notebook sessions run as datascience user with limited privileges.
* Evaluate Options:
* A: False-Yum isn't available; pip is the primary tool.
* B: True-Custom repos work with proper network config.
* C: False-No root access; managed environment.
* D: True-PyPI packages installable with internet (NAT Gateway).
* E: False-Youcaninstall beyond preinstalled; likely meant opposite.
* Reasoning: B and D are true; E's intent seems reversed (common exam error)-corrected to B, D.
* Conclusion: B, D (assuming E typo).
OCI documentation states: "Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn't supported." E contradicts capability-corrected, B and D are accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".

Detailed Answer in Step-by-Step Solution:
* Objective: Identify three annotation methods in OCI Data Labeling for images.
* Understand Data Labeling: Supports image annotations for ML.
* Evaluate Options:
* A: Semantic segmentation with boxes-Incorrect; segmentation is pixel-based, not boxes.
* B: Single label (classification)-Supported-correct.
* C: No bounding boxes-False; boxes are supported.
* D: Object detection with boxes-Supported-correct.
* E: Multiple labels (multi-label)-Supported-correct.
* Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
* Conclusion: B, D, E are correct.
OCI documentation states: "Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E)." A misdefines segmentation, C contradicts support-only B, D, E are valid per OCI's Data Labeling features.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

Detailed Answer in Step-by-Step Solution:
* Objective: Identify an open model format for cross-platform ML model execution.
* Evaluate Options:
* A. PySpark: A big data framework, not a model format.
* B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
* C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
* D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
* Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
* Conclusion: D is the correct choice.
ONNX (D) is "an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow," per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats-only ONNX ensures cross-platform compatibility.
Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

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".