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| Section | Weight | Objectives |
|---|---|---|
| MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Model Development and Deployment | 30% | - Model training
|
| OCI Data Science Service | 30% | - Model catalog
|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
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NEW QUESTION # 89
In machine learning, what is the primary difference between supervised and unsupervised learning?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the key difference between supervised and unsupervised learning.
Define Types:
Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
Evaluate Options:
A: Labeled vs. unlabeled—Core distinction, correct.
B: Monitoring—Misleading, not the primary difference.
C: Image recognition—False, supervised applies broadly.
D: Data Engineer—Irrelevant to learning type.
Reasoning: A captures the foundational data difference.
Conclusion: A is correct.
OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".
NEW QUESTION # 90
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
Answer: C
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 # 91
You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Scale up a notebook session without losing work.
Understand Persistence: Block volume stores session data (e.g., /home/datascience).
Evaluate Options:
A: Recreating work—inefficient, risks loss.
B: Local download/upload—cumbersome, unnecessary.
C: Use block volume persistence, scale up—efficient, preserves work—correct.
D: Object Storage—extra steps, not needed with block volume.
Reasoning: C leverages OCI’s built-in persistence for seamless scaling.
Conclusion: C is correct.
OCI documentation states: “Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work.” A loses data, B and D add complexity—only C is optimal.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 92
Which of the following best describes the principal goal of data science?
Answer: D
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 # 93
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Securely authenticate a recurring OCI Job.
Evaluate Options:
A: PAR—Limited duration, insecure for recurring jobs.
B: Resource principal—Secure, managed auth for Jobs—correct.
C: Personal config—Unscalable, security risk.
D: Vault with personal keys—Complex, still uses user creds.
Reasoning: B uses OCI’s native, secure resource principal mechanism.
Conclusion: B is correct.
OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".
NEW QUESTION # 94
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