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| Section | Objectives |
|---|---|
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
| 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 |
| Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Prepare and manage datasets |
| Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
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NEW QUESTION # 48
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels. Which types of custom models can be trained?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language service.
Understand OCI Language: Focuses on text analysis, not images.
Evaluate Options:
A: Image classification—Incorrect; Language is text-based.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment—Pretrained, not custom; NER ok—incorrect.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s custom text capabilities.
Conclusion: B is correct.
OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
NEW QUESTION # 49
Which statement about resource principals is true?
Answer: B
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 # 50
You have just completed analyzing a set of images by using Oracle Cloud Infrastructure (OCI) Data Labeling, and you want to export the annotated dat a. Which TWO formats are supported?
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify export formats for OCI Data Labeling.
Understand Export: Annotated data is exported for ML use.
Evaluate Options:
A: CONLL V2003—Text-specific, not OCI-supported.
B: COCO—Standard for image annotations—correct.
C: Proprietary JSON—OCI’s default format—correct.
D: Spacy—NLP format, not supported.
Reasoning: B and C are image-compatible per OCI docs.
Conclusion: B and C are correct.
OCI documentation states: “Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service.” CONLL (A) and Spacy (D) are text/NLP-focused—not supported for OCI’s image annotations.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".
NEW QUESTION # 51
Which is NOT a part of Observability and Management Services?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-Observability and Management (O&M) service in OCI.
Understand O&M: Includes monitoring, logging, events tools.
Evaluate Options:
A: Event Services—Triggers actions, part of O&M—correct.
B: OCI Management Service—Not a defined O&M service—incorrect.
C: Logging Analytics—Log analysis, O&M component—correct.
D: Logging—Log collection, O&M component—correct.
Reasoning: B isn’t listed in OCI’s O&M suite—others are.
Conclusion: B is correct (not part of O&M).
OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.
1: Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".
NEW QUESTION # 52
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,E
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 # 53
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