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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Prepare and manage datasets - Build, train, and evaluate machine learning models |
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
| OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
| 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 |
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NEW QUESTION # 127
Which Oracle Cloud Service provides restricted access to target resources?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for restricted resource access.
Evaluate Options:
A: Bastion—Secure, temporary access to resources—correct.
B: Internet Gateway—Public access, not restricted.
C: Load Balancer—Distributes traffic, not access control.
D: SSL Certificate—Secures comms, not access.
Reasoning: Bastion limits access (e.g., SSH) to specific targets.
Conclusion: A is correct.
OCI documentation states: “OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH.” B, C, and D don’t restrict—only A fits per OCI’s security services.
1: Oracle Cloud Infrastructure Bastion Documentation, "Overview".
NEW QUESTION # 128
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the Oracle service to sort document images by language.
Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.
Evaluate Options:
A . Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.
B . OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.
C . OCI Speech: Transcribes audio to text—not applicable to images.
D . OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.
Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
Conclusion: B is correct.
OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".
NEW QUESTION # 129
How can you collaborate with team members in OCI Data Science Workspace?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
Evaluate Options:
A: Access control—Possible but not primary collaboration.
B: Version control (e.g., Git)—Standard for code sharing—correct.
C: Shared instance—Not supported; sessions are single-user.
D: Chat/video—Not a feature of OCI Data Science.
Reasoning: B leverages Git for team collaboration—OCI’s recommended method.
Conclusion: B is correct.
OCI documentation states: “Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks.” A is limited, C isn’t feasible, and D isn’t available—only B matches OCI’s collaboration approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".
NEW QUESTION # 130
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining model performance and improve churn prediction.
Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.
Evaluate Options:
A . Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.
B . Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.
C . Operational monitoring: Tracks system health (e.g., latency), not prediction quality.
D . Retrain the model: Updates model with new data—directly improves accuracy—correct.
E . Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.
Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.
Conclusion: A and D are correct.
OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 131
Which of the following best describes the principal goal of data science?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define data science’s main goal.
Evaluate Options:
A: Archiving—Not the focus; too narrow.
B: Analyze for insights/business value—Core purpose—correct.
C: Prep for analytics—Means, not the end goal.
D: Output-focused—Vague, incomplete.
Reasoning: B captures the actionable insight generation central to data science.
Conclusion: B is correct.
OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage-focused, C is preparatory, and D is unclear—only B reflects the principal goal per OCI’s mission.
1: Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".
NEW QUESTION # 132
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