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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| Topic 2: MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Topic 3: Model Development and Deployment | 30% | - Model training
|
| Topic 4: OCI Data Science Service | 30% | - Projects and notebooks
|
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NEW QUESTION # 117
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: A
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 # 118
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Answer: C,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI—Supports Data Science tasks—possible but not primary.
B: ADS SDK—Designed for model catalog ops—correct.
C: ODSC CLI—Not standard; likely typo for OCI CLI.
D: Console—GUI for catalog uploads—correct.
E: OCI Python SDK—Programmatic catalog access—correct.
F: Git CLI—Version control, not catalog-related.
Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
NEW QUESTION # 119
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?
Answer: C,F
Explanation:
Detailed Answer in Step-by-Step Solution:
Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases.
Evaluate Statements:
A: True—Open Data includes text and image datasets (e.g., geospatial images).
B: False—Video and other formats may be available depending on the dataset; no strict exclusion exists.
C: False—Datasets may include metadata, but code/tooling examples aren’t guaranteed.
D: True—It’s designed for data scientists and analysts who work with datasets.
E: False—It’s not a user-contributed repository; it’s curated by Oracle.
F: False—Open Data is free and public, not subscription-based.
Select Two: A and D align with the service’s purpose and offerings.
OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").
NEW QUESTION # 120
Which step is a part of the AutoML pipeline?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
NEW QUESTION # 121
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining prediction accuracy and verify model performance.
Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.
Evaluate Options:
A . Retrain the model: Uses new data to update the model—fixes accuracy—correct.
B . Validate with recent data: Tests performance but doesn’t fix—diagnostic only.
C . Drift monitoring: Detects data distribution shifts—verifies cause—correct.
D . Redeploy the model: Repeats deployment, doesn’t address root cause.
E . Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.
Conclusion: A and C are correct.
OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 122
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