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".

OCI Logging integrates with Data Science jobs and model deployments to capture logs, errors, and

runtime events, enabling observability and debugging of MLOps workflows.

Detailed Answer in Step-by-Step Solution:

Objective: Efficiently increase storage for an OCI Job.

Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable. Evaluate

Options:

A: False—Jobs can’t edit storage post-creation; it’s fixed. B: False—No environment variable adjusts

storage size.

C: True—Create a new job with larger storage (e.g., 200 GB) and run it. D: False—Refactoring code is

inefficient compared to increasing storage. Reasoning: C is the standard OCI process for adjusting

resources.

Conclusion: C is correct.

OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g.,

block volume size). To increase it, create a new job with a larger storage configuration and initiate a new

run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue— only C

is efficient.

: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the interactive coding environment in OCI Data Science. Evaluate Options:

A: Model Catalog stores models—not for coding. B: Jobs run predefined tasks—not interactive.

C: Notebook Sessions provide JupyterLab for coding and training—interactive. D: Projects organize

work—not a coding environment.

Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.

Conclusion: C is correct.

OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data

scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for

storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.

: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining model performance and improve churn prediction.

Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.

Evaluate Options:

A . Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—

correct.

B . Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.

C . Operational monitoring: Tracks system health (e.g., latency), not prediction quality.

D . Retrain the model: Updates model with new data—directly improves accuracy—correct.

E . Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective. Reasoning:

A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.

Conclusion: A and D are correct.

OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model

performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address

drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align

with OCI’s model maintenance strategy.

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

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- ofweek), distinct from tuning or interpretation.

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 the initial data science step.

Define Process: Starts with problem definition, then data and modeling. Evaluate Options:

A: Data collection—Second step after problem definition. B: Modeling—Later stage.

C: Hypothesis—Sets the goal, first step—correct. D: Data owners—Collaboration, not the start.

Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”). Conclusion: C is correct.

OCI documentation states: “The data science process begins with defining an analytical hypothesis to

address a business problem, followed by data collection and analysis.” C precedes A, B, and D— aligning

with OCI’s structured approach.

: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about OCI Anomaly Detection.

Understand Service: Detects anomalies in multivariate data (e.g., time series). Evaluate Options:

A: False—Accepted types are CSV/JSON, not SQL/Python.

B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly. C: True—Used for fraud,

intrusions, and sensor anomalies (key use cases).

D: False—Trained on customer data only, not general datasets. Reasoning: C aligns with documented

applications; others misalign. Conclusion: C is correct.

OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series

data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is

incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C

matches the service’s purpose.

: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".

Detailed Answer in Step-by-Step Solution:

Objective: Determine where to specify the conda environment for an OCI model deployment. Understand

Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.

Evaluate Options:

A . score.py: Contains inference logic (e.g., load_model(), predict())—not for environment specs.

B . runtime.yaml: Defines deployment runtime, including conda environment path—correct.

C . requirements.txt: Lists pip dependencies—not used in OCI for conda environments.

D . model_artifact_validate.py: Not a standard artifact; doesn’t exist in OCI deployment.

Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)—failure to set

this causes deployment errors. Conclusion: B is correct.

OCI documentation states: “The runtime.yaml file in a model artifact specifies the runtime environment,

including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2),

ensuring the deployed model uses the correct dependencies.” score.py (A) handles inference,

requirements.txt (C) is for pip (not conda in OCI), and D isn’t valid—only B addresses the conda issue

per OCI’s deployment process.

: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

OCI Vault is used to securely store and manage sensitive data such as passwords, API tokens, and

encryption keys. It enables controlled access and integrates with other OCI services like Data Science to

ensure credentials are not hardcoded into notebooks.