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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking |
| Topic 2: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation |
| Topic 4: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
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NEW QUESTION # 12
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Answer: A,E,F
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI—Supports Data Science tasks—possible but not primary.
B: ADS SDK—Designed for model catalog ops—correct.
C: ODSC CLI—Not standard; likely typo for OCI CLI.
D: Console—GUI for catalog uploads—correct.
E: OCI Python SDK—Programmatic catalog access—correct.
F: Git CLI—Version control, not catalog-related.
Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
NEW QUESTION # 13
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: A
Explanation:
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 14
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Securely authenticate a recurring OCI Job.
Evaluate Options:
A: PAR—Limited duration, insecure for recurring jobs.
B: Resource principal—Secure, managed auth for Jobs—correct.
C: Personal config—Unscalable, security risk.
D: Vault with personal keys—Complex, still uses user creds.
Reasoning: B uses OCI’s native, secure resource principal mechanism.
Conclusion: B is correct.
OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".
NEW QUESTION # 15
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining prediction accuracy and verify model performance.
Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.
Evaluate Options:
A . Retrain the model: Uses new data to update the model—fixes accuracy—correct.
B . Validate with recent data: Tests performance but doesn’t fix—diagnostic only.
C . Drift monitoring: Detects data distribution shifts—verifies cause—correct.
D . Redeploy the model: Repeats deployment, doesn’t address root cause.
E . Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.
Conclusion: A and C are correct.
OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 16
Which two statements are true about published conda environments?
Answer: B,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify true statements about published conda environments in OCI.
Understand Published Conda: Custom envs shared via Object Storage.
Evaluate Options:
A: False—Users create them, not OCI-curated.
B: True—odsc conda init sets up access to published envs.
C: False—Object Storage, not notebook, is the source.
D: False—Can create from scratch, not just modifications.
E: True—Conda env vars extend job configs.
Reasoning: B configures access, E enhances flexibility—match OCI behavior.
Conclusion: B and E are correct.
OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.
1: Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".
NEW QUESTION # 17
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