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| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| 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 |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Model development, training, and evaluation - Data preparation, exploration, and transformation - Deploy models and manage endpoints - Model saving, cataloging, and versioning |
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NEW QUESTION # 92
What do you use the score.py file for?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the purpose of score.py in OCI Data Science model deployment.
Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.
Evaluate Options:
A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.
B: score.py contains the inference logic (e.g., load_model(), predict())—correct.
C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.
D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.
Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
Conclusion: B is the correct purpose.
The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".
NEW QUESTION # 93
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Run a complex Python project as an OCI Job.
Evaluate Options:
A: Auto-identification—False; entrypoint must be set.
B: Rewrite—Unnecessary, inefficient.
C: Auto-executable—False; needs explicit entrypoint.
D: ZIP with entrypoint—Correct, flexible approach.
Reasoning: D preserves structure, specifies execution.
Conclusion: D is correct.
OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".
NEW QUESTION # 94
Which statement about resource principals is true?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
Evaluate Options:
A: False—Resource principals eliminate manual credential management.
B: False—They’re secure, leveraging IAM policies, not less secure than API keys.
C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).
D: True—Resource principals are an IAM feature authorizing resources as actors.
Reasoning: D captures the essence of resource principals as an IAM mechanism.
Conclusion: D is correct.
OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".
NEW QUESTION # 95
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Diagnose a network timeout during pip install in a notebook session.
Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.
Evaluate Options:
A: NAT Gateway—Provides internet access for private subnets—correct fix.
B: Service Gateway—Accesses OCI services privately, not public internet.
C: FastConnect—Links to on-premises, not public internet.
D: VNIC—Essential but present by default; doesn’t solve internet access.
Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
Conclusion: A is correct.
OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".
NEW QUESTION # 96
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: A,B
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 # 97
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