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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 2: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 3: Model Development and Deployment | 30% | - Model training
|
| Topic 4: Machine Learning Fundamentals | 20% | - Unsupervised learning
|
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NEW QUESTION # 77
Which of these is a unique feature of the published conda environment?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.
Evaluate Options:
A: Vague—All conda environments can address use cases; not unique to “published.” B: Incorrect—Availability on reactivation applies to session persistence, not publishing.
C: Correct—Publishing saves the environment to Object Storage for sharing/reuse.
D: Incorrect—Block volumes store session data, not published environments.
Reasoning: The unique aspect of “published” environments is their storage in Object Storage (via odsc conda publish), enabling team access.
Conclusion: C is the distinctive feature.
The OCI Data Science documentation highlights that “published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users.” This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.
NEW QUESTION # 78
Why is data sampling useful for data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the primary benefit of data sampling.
Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.
Evaluate Options:
A: Small batches reduce resources—True but not the main purpose.
B: Reduces storage—Incidental, not the goal.
C: Representative subset for faster, accurate models—Core purpose of sampling.
Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
Conclusion: C is correct.
OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
NEW QUESTION # 79
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the WAF component that controls request actions based on criteria.
Understand WAF Components:
Protection Rules: Define conditions and actions (e.g., allow, block, log).
Bot Management: Handles bot traffic, not general request rules.
Origin: Backend server endpoint, not rule-based.
WAF Policy: Umbrella config, but rules specify actions.
Evaluate Options:
A: Protection rules—Set specific criteria and actions—correct.
B: Bot Management—Bot-specific, not general requests.
C: Origin—Defines source, not actions.
D: WAF policy—Broad config, not the granular rules.
Reasoning: Protection rules directly manage request behavior—fit the requirement.
Conclusion: A is correct.
OCI documentation states: “Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.” Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn’t the action specifier—only A aligns with OCI’s WAF configuration.
1: Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".
NEW QUESTION # 80
You are a researcher who requires access to large datasets. Which OCI service would you use?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for accessing large public datasets.
Evaluate Options:
A: Oracle Databases—General-purpose, not dataset-focused.
B: ADW—Analytics warehouse, not a dataset repository.
C: OCI Data Science—ML platform, not a dataset provider.
D: Oracle Open Data—Free, public datasets (e.g., geospatial).
Reasoning: Open Data provides pre-existing large datasets for research.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers.” Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML—not datasets—only Open Data (D) fits.
1: Oracle Cloud Infrastructure Open Data Documentation, "Overview".
NEW QUESTION # 81
Which THREE types of data are used for Data Labeling?
Answer: B,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).
Understand Data Labeling: Annotates data for ML—focuses on specific types.
Evaluate Options (Assuming Typical Set):
A: Audio—Not supported—incorrect.
B: Text Document—Supported (e.g., NER)—correct.
C: Images—Supported (e.g., object detection)—correct.
D: Graphs—Not a standard type—incorrect.
Assumed E: Videos—Supported but missing—adjust to fit.
Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.
Conclusion: B, C (third likely video, missing).
OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
NEW QUESTION # 82
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