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| Section | Objectives |
|---|---|
| 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 |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Prepare and manage datasets - Build, train, and evaluate machine learning models |
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
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NEW QUESTION # 53
Which statement about resource principals is true?
Answer: A
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 # 54
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials needed.
Analyze 404 Error: Indicates an authorization failure—likely missing permissions or misconfigured resource principal.
Evaluate Options:
A: True—Dynamic group must include notebook sessions (e.g., resource.type = 'datasciencenotebooksession') to authenticate.
B: False—Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
C: True—Policy must grant manage data-science-models to the dynamic group for catalog access.
D: False—Service gateway ensures network access, but 404 is auth-related, not connectivity.
E: False—Resource principal uses dynamic group policies, not user group policies.
Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth—others are tangential.
Conclusion: A and C are correct.
OCI documentation states: “To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = 'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.” B is unrelated (storage location), D is network-focused, and E applies to user auth—not resource principal. A 404 error flags missing auth, fixed by A and C.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".
NEW QUESTION # 55
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an open model format for cross-platform ML model execution.
Evaluate Options:
A . PySpark: A big data framework, not a model format.
B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
Conclusion: D is the correct choice.
ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".
NEW QUESTION # 56
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?
Answer: A,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Prevent deployment failure due to utilization limits.
Understand Utilization: High load requires capacity or throttling.
Evaluate Options:
A: More instances—Scales horizontally—correct.
B: Delete—Stops service, not a fix—incorrect.
C: Fewer instances—Worsens issue—incorrect.
D: Larger VM—Scales vertically—correct.
E: Reduce bandwidth—Controls load—correct.
Reasoning: A and D increase capacity, E manages demand—effective trio.
Conclusion: A, D, E are correct.
OCI documentation states: “To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.” B stops service, C reduces capacity—only A, D, E align with OCI’s deployment scaling options.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
NEW QUESTION # 57
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine when an OCI Data Science pipeline can start.
Understand Pipelines: They’re workflows with defined steps, executed on demand or scheduled.
Evaluate Options:
A: Once created, a pipeline can be run immediately—correct.
B: “During run state” implies it’s already running—illogical.
C: “After active state” is unclear; pipelines run when triggered, not post-state.
D: “Before active state” is vague—creation precedes running.
Reasoning: Pipelines are executable post-creation via UI/CLI—simplest interpretation is A.
Conclusion: A is correct.
OCI Data Science documentation states: “After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK.” B, C, and D misalign with this—running starts post-creation (A), not during/after ambiguous states.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".
NEW QUESTION # 58
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