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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Development and Deployment | 30% | - Model training
|
| Topic 2: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 3: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 4: OCI Data Science Service | 30% | - Model catalog
|
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NEW QUESTION # 90
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two methods to enhance Object Storage durability.
Understand Durability: Ensures data isn’t lost—focus on redundancy and protection.
Evaluate Options:
A: RAID1—Block volume feature, not Object Storage.
B: Encryption—Secures data, not durability.
C: Versioning—Retains old versions, prevents loss—correct.
D: Limit delete—Prevents accidental deletion—correct.
E: Client encryption—Secures, not durability-focused.
Reasoning: C and D directly protect against data loss—durability-focused.
Conclusion: C and D are correct.
OCI documentation states: “Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss.” A isn’t applicable, B and E focus on security—only C and D enhance durability per OCI’s storage features.
1: Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".
NEW QUESTION # 91
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Get all AutoML trial results.
Understand AutoML: Trials include hyperparameter tuning outcomes.
Evaluate Options:
A: print_trials()—Displays all trial results—correct.
B: visualize_tuning_trials()—Visualizes tuning, not full list.
C: visualize_adaptive_sampling_trials()—Specific to sampling, not all trials.
D: visualize_algorithm_selection_trials()—Specific to algorithms, not all.
Reasoning: A provides comprehensive trial output.
Conclusion: A is correct.
OCI AutoML documentation states: “print_trials() outputs a table of all trials performed, including hyperparameters and scores.” Visualization methods (B, C, D) focus on specific aspects—only A gives the full list.
1: Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".
NEW QUESTION # 92
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose the better model for cancer prediction based on metrics.
Understand Metrics:
Accuracy: Overall correct predictions.
Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).
Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.
Evaluate Models:
Model 1: 80% accuracy, 70% recall—Misses more cancers.
Model 2: 75% accuracy, 85% recall—Misses fewer cancers.
Evaluate Options:
A: High recall—True, but lacks context.
B: High accuracy—Misses recall’s importance.
C: Recall’s impact—Correct for cancer use case—best.
D: Lesser recall impact—Incorrect for this priority.
Reasoning: C emphasizes recall’s critical role—aligns with medical needs.
Conclusion: C is correct.
OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.
1: Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".
NEW QUESTION # 93
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: C
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 # 94
What is feature engineering in machine learning used for?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 95
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