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| Section | Weight | Objectives |
|---|---|---|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| OCI Data Science Service | 30% | - Model catalog
|
| Model Development and Deployment | 30% | - Model training
|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
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NEW QUESTION # 28
In machine learning, what is the primary difference between supervised and unsupervised learning?
Answer: A
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 # 29
Where do calls to stdout and stderr from score.py go in the model deployment?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate stdout/stderr output from score.py in deployment.
Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
Evaluate Options:
A: False—No VM file is defined by default; logs go to OCI Logging.
B: False—Console displays UI, not raw logs.
C: False—Cloud Shell is a separate tool, not a log destination.
D: True—Predict logs in OCI Logging capture stdout/stderr.
Reasoning: OCI centralizes logs in its Logging service for deployments.
Conclusion: D is correct.
OCI documentation states: “During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings.” A, B, and C don’t align with this managed logging approach—only D is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".
NEW QUESTION # 30
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate the lifecycle stage for transforming timestamps into categories.
Understand Lifecycle:
Model Type Selection: Choosing algorithms—post-data prep.
Model Validation: Evaluating performance—post-training.
Data Access: Retrieving raw data—pre-transformation.
Feature Engineering: Creating new features—correct.
Evaluate Options:
A: Too late—incorrect.
B: Post-model—incorrect.
C: Pre-transformation—incorrect.
D: Feature creation—correct.
Reasoning: Converting timestamps to time periods is feature engineering.
Conclusion: D is correct.
OCI documentation states: “Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs.” A, B, and C occur at different stages—only D fits OCI’s lifecycle for this task.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".
NEW QUESTION # 31
Which statement is true about standards?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about standards in an OCI context (likely governance/security).
Understand Standards: Rules or benchmarks, often compliance-related.
Evaluate Options:
A: Auditable—True; standards are checked for adherence.
B: Result of requirements—Partially true, but not always.
C: Methods/instructions—More procedural, not defining standards.
D: Foundation of governance—Broad, not specific to standards.
Reasoning: A is universally true—standards face audits (e.g., SOC, ISO).
Conclusion: A is correct.
OCI documentation notes: “Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations.” B is a source, C describes procedures, D is too vague—only A is consistently true per OCI’s compliance framework.
1: Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".
NEW QUESTION # 32
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Data Science Jobs.
Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
Evaluate Options:
A: True—Jobs provision OCI compute resources on-demand for task execution.
B: False—Users define custom tasks (e.g., Python scripts), not limited to standard ones.
C: False—Infrastructure is fully managed by OCI, not user-managed.
D: False—Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
Reasoning: A reflects OCI’s managed, on-demand provisioning model for Jobs.
Conclusion: A is correct.
The OCI Data Science documentation states: “Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility—only A accurately describes Jobs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".
NEW QUESTION # 33
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