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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 - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources - Create and configure Data Science projects |
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Prepare and manage datasets |
>> 1Z0-1110-26 Simulationsfragen <<
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11. Frage
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%
Antwort: A
Begründung:
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".
12. Frage
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?
Antwort: C
Begründung:
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".
13. Frage
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?
Antwort: D
Begründung:
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".
14. Frage
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?
Antwort: A,C
Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining model performance and improve churn prediction.
Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.
Evaluate Options:
A . Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.
B . Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.
C . Operational monitoring: Tracks system health (e.g., latency), not prediction quality.
D . Retrain the model: Updates model with new data—directly improves accuracy—correct.
E . Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.
Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.
Conclusion: A and D are correct.
OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
15. Frage
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
Antwort: B
Begründung:
Detailed Answer in Step-by-Step Solution:
Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.
Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").
16. Frage
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