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| Section | Objectives |
|---|---|
| Topic 1: Apply MLOps Practices | - Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models |
| Topic 2: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
| Topic 3: OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models |
| Topic 5: 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 # 109
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service to sort images by language.
Evaluate Options:
A: Digital Assistant—Chatbots, not image/language processing.
B: Vision—Image analysis (e.g., object detection), not language sorting.
C: Speech—Audio-to-text, not image-based.
D: Language—Text analysis (e.g., language detection) after OCR—correct.
Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.
Conclusion: D is correct.
OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection".
NEW QUESTION # 110
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 # 111
What is the first step in the data science process?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the initial data science step.
Define Process: Starts with problem definition, then data and modeling.
Evaluate Options:
A: Data collection—Second step after problem definition.
B: Modeling—Later stage.
C: Hypothesis—Sets the goal, first step—correct.
D: Data owners—Collaboration, not the start.
Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).
Conclusion: C is correct.
OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".
NEW QUESTION # 112
Which architecture is based on the principle of “never trust, always verify”?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the architecture with “never trust, always verify.” Evaluate Options:
A: Federated identity—Shares auth, not verification-focused.
B: Zero trust—Explicitly “never trust, always verify”—correct.
C: Fluid perimeter—Adaptive, not the core principle.
D: Defense in depth—Layered, not verification-centric.
Reasoning: Zero trust matches the stated principle exactly.
Conclusion: B is correct.
OCI documentation states: “Zero trust (B) architecture operates on ‘never trust, always verify,’ requiring continuous authentication and authorization.” A, C, and D have different focuses—only B aligns with OCI’s security philosophy.
1: Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".
NEW QUESTION # 113
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify supported annotation classes in OCI Data Labeling.
Understand Tool: Supports image/text annotations for ML.
Evaluate Options:
A: Object detection—Yes (bounding boxes).
B: Named entity—Text-specific, not primary for images.
C: Classification—Yes (labels for images/text).
D: Key-point—Not listed in OCI docs.
E: Polygonal—Not explicitly supported.
F: Semantic segmentation—Yes (pixel-level).
Reasoning: A, C, F match OCI’s image/text focus.
Conclusion: A, C, F are correct.
OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".
NEW QUESTION # 114
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