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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
| Topic 2: OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Prepare and manage datasets - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models |
| Topic 4: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
| Topic 5: 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 |
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NEW QUESTION # 154
You want to create a user group for a team of external data science consultants. The consultants should only have the ability to see Data Science resource details but not have the ability to create, delete, or update Data Science resources. What verb should you write in the policy?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.” Define Requirement: Consultants need view-only access without modification rights.
Evaluate Verbs:
A . Use: Allows interaction (e.g., running jobs)—too permissive.
B . Inspect: Grants view-only access to resource details—matches requirement.
C . Manage: Full control (create, update, delete)—too permissive.
D . Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.
Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.
In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").
NEW QUESTION # 155
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: C
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 # 156
Which of the following analytical and statistical techniques do data scientists commonly use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify common data science techniques.
Define Techniques:
Classification: Predicts categories (e.g., spam vs. not).
Regression: Predicts continuous values (e.g., sales).
Clustering: Groups data (e.g., customer segments).
Evaluate Options:
A, B, C: All are standard ML/statistical methods.
D: Encompasses all—correct as they’re widely used.
Reasoning: These are foundational in data science workflows.
Conclusion: D is correct.
OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).
1: Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".
NEW QUESTION # 157
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
Evaluate Options:
A . Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.
B . Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
C . Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
D . Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.
NEW QUESTION # 158
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine minimum log storage duration before archiving in Logging Analytics.
Understand Logging Analytics: Logs are active before archival.
Evaluate Options:
A: 60 days—Too long for minimum.
B: 10 days—Too short.
C: 30 days—Standard minimum—correct.
D: 15 days—Not OCI’s default.
Reasoning: 30 days is OCI’s documented minimum active period.
Conclusion: C is correct.
OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 159
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