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| Section | Weight | Objectives |
|---|---|---|
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science |
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Model development, training, and evaluation - Data preparation, exploration, and transformation |
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NEW QUESTION # 96
You want to write a program that performs document analysis tasks such as extracting text and tables from a document. Which Oracle AI service would you use?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service for text and table extraction from documents.
Evaluate Options:
A: Language—Text analysis, not extraction—incorrect.
B: Digital Assistant—Chatbots, not document tasks—incorrect.
C: Speech—Audio transcription, not documents—incorrect.
D: Vision—OCR for text/tables—correct.
Reasoning: Vision’s OCR extracts text and tables from document images.
Conclusion: D is correct.
OCI documentation states: “OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks.” A analyzes text post-extraction, B and C are unrelated—only D fits per OCI’s AI services.
1: Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".
NEW QUESTION # 97
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently increase storage for an OCI Job.
Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
Evaluate Options:
A: False—Jobs can’t edit storage post-creation; it’s fixed.
B: False—No environment variable adjusts storage size.
C: True—Create a new job with larger storage (e.g., 200 GB) and run it.
D: False—Refactoring code is inefficient compared to increasing storage.
Reasoning: C is the standard OCI process for adjusting resources.
Conclusion: C is correct.
OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".
NEW QUESTION # 98
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
Evaluate Components:
A: User groups are created for role-based access—automated.
B: Dynamic groups (e.g., for notebook sessions) are included—automated.
C: Individual users require manual creation via IAM—not automated.
D: Basic policies (e.g., access to Data Science resources) are included—automated.
Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
Conclusion: C is the exception.
The OCI Resource Manager template for Data Science “automatically provisions user groups, dynamic groups, and policies for basic use cases,” but “individual users must be created separately in IAM and assigned to groups.” C is the only item not handled by the template, per the documentation.
1: Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".
NEW QUESTION # 99
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Transcribe Spanish video audio and identify profanity.
Evaluate Options:
A: Anomaly Detection—Not for transcription or text analysis.
B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.
C: Translation—Translates text, not transcription.
D: Language—Analyzes text (e.g., profanity), but needs transcribed input.
Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
Conclusion: B is correct for transcription.
OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.
1: Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
NEW QUESTION # 100
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Predict AutoML’s output when time runs out during tuning.
Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
Evaluate Options:
A: Best-known config—Logical, reflects optimization goal—correct.
B: Last config—Ignores prior better results—incorrect.
C: Minimum learning rate—Arbitrary, not performance-based.
D: Random—Defeats tuning purpose.
Reasoning: AutoML prioritizes the best config found within the budget.
Conclusion: A is correct.
OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.
1: Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".
NEW QUESTION # 101
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