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| Section | Objectives |
|---|---|
| Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Prepare and manage datasets - Automate machine learning workflows and pipelines |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources - Create and configure Data Science projects |
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103. Frage
Which Security Zone policy is NOT valid?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Spot the invalid Security Zone policy.
Understand Security Zones: Enforce strict security—resources can’t leave easily.
Evaluate Options:
A: Boot volume move—Not allowed; violates zone rules—invalid.
B: Compute no-move—True, enforces security.
C: No public access—True, standard policy.
D: Auto-backup—True, required for compliance.
Reasoning: A contradicts Security Zone immutability.
Conclusion: A is incorrect.
OCI documentation states: “Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment—B, C, and D are enforced policies.” A violates OCI’s Security Zone restrictions—only A is not valid.
1: Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".
104. Frage
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for a PySpark app in OCI Data Science.
Evaluate Steps:
Launch notebook: First—provides the environment.
Install PySpark conda: Second—sets up Spark libraries.
Configure core-site.xml: Third—connects to data (e.g., Object Storage).
Develop app: Fourth—writes the PySpark code.
Data Flow: Fifth—optional scaling, post-development.
Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
Reasoning: Notebook first, then setup, coding, and scaling.
Conclusion: D is correct.
OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".
105. Frage
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
Antwort: C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Define the role of score.py in OCI model artifacts.
Understand Artifacts: score.py is key for deployment runtime.
Evaluate Options:
A: Infra config—Handled by OCI settings, not score.py.
B: Inference logic—Correct; runs load_model(), predict().
C: Scaling—Set in deployment, not score.py.
D: Dependencies—In runtime.yaml, not score.py.
Reasoning: B aligns with score.py’s execution role.
Conclusion: B is correct.
OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".
106. Frage
Which architecture is based on the principle of “never trust, always verify”?
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the architecture with “never trust, always verify.” Evaluate Options:
A: Federated identity—Shares auth, not verification-focused.
B: Zero trust—Explicitly “never trust, always verify”—correct.
C: Fluid perimeter—Adaptive, not the core principle.
D: Defense in depth—Layered, not verification-centric.
Reasoning: Zero trust matches the stated principle exactly.
Conclusion: B is correct.
OCI documentation states: “Zero trust (B) architecture operates on ‘never trust, always verify,’ requiring continuous authentication and authorization.” A, C, and D have different focuses—only B aligns with OCI’s security philosophy.
1: Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".
107. Frage
Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.
Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.
Evaluate Options:
A: Correct—Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources.
B: Incorrect—Infrastructure is managed by OCI, not the customer.
C: Incorrect—No third-party cloud integration; it’s OCI-specific.
D: Incorrect—Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines).
Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with A.
Conclusion: A accurately reflects the service’s purpose and operation.
OCI Data Science Jobs “allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure.” This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.
1: Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.
108. Frage
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