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| Section | Objectives |
|---|---|
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
| Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Prepare and manage datasets - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models |
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NEW QUESTION # 11
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define LIME’s core concept.
Understand LIME: Explains individual predictions with local surrogate models.
Evaluate Options:
A: Complex global, simple local—Correct LIME principle.
B: Agnosticism—True but not the key idea.
C: Global/local similarity—False.
D: Local vs. global agnosticism—Incorrect distinction.
Reasoning: A captures LIME’s local approximation focus.
Conclusion: A is correct.
OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
NEW QUESTION # 12
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Apply all recommended transformations from get_recommendations() in ADS.
Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
Evaluate Options:
A: Returns transformed data—Not for applying—incorrect.
B: Sklearn-style, not ADS-specific—incorrect.
C: auto_transform()—Applies all recommendations—correct.
D: Visualizes, doesn’t apply—incorrect.
Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
Conclusion: C is correct.
OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".
NEW QUESTION # 13
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define the role of score.py in OCI model artifacts.
Understand Artifacts: score.py is key for deployment runtime.
Evaluate Options:
A: Infra config—Handled by OCI settings, not score.py.
B: Inference logic—Correct; runs load_model(), predict().
C: Scaling—Set in deployment, not score.py.
D: Dependencies—In runtime.yaml, not score.py.
Reasoning: B aligns with score.py’s execution role.
Conclusion: B is correct.
OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".
NEW QUESTION # 14
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Match a cache rule criterion for exact URL path and query.
Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
Evaluate Options:
A: Contains—Partial match, not exact.
B: Is—Exact match of full URL (path + query)—correct.
C: Ends with—Matches end, not full URL.
D: Starts with—Matches start, not full URL.
Reasoning: “URL_IS” checks exact equality—fits requirement.
Conclusion: B is correct.
OCI documentation states: “The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value.” A, C, and D are partial matches—only B ensures identical concatenation per OCI’s caching config.
1: Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".
NEW QUESTION # 15
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Fix network timeout for pip install in a notebook.
Evaluate Options:
A: FastConnect—On-premises link, not public internet.
B: VNIC—Default, not the issue.
C: NAT Gateway—Grants internet access—correct.
D: Service Gateway—OCI services, not PyPI.
Reasoning: C enables outbound traffic to public repos.
Conclusion: C is correct.
OCI documentation states: “A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI.” A, B, and D don’t provide this—only C resolves the timeout.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".
NEW QUESTION # 16
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