作為IT業界的頂級公司，Oracle 通過其認證確定了產品專家的標準，可以說 Oracle 在業界的聲望和 Oracle 產品的市場佔有率提升了其認證工程師的含金量，一個 Oracle 認證工程師獲取在優秀企業工作的機會比普通工程師大60％－80％，平均薪水高出30％-50％。世界500強企業中，有超過2/3的企業選擇了Oracle電子商務軟體產品作為其核心的運用。因此，獲得1Z0-1110-26 的證照，即使在強手林立的競爭環境中，你同樣能夠脫穎而出。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Topic 2: Apply MLOps Practices | 20% | - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model development, training, and evaluation - Data preparation, exploration, and transformation - Use AutoML and built-in algorithms - Model saving, cataloging, and versioning |
| Topic 4: Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
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問題 #102
Triggering a PagerDuty notification as part of Monitoring is an example of what in the OCI Console?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Classify a PagerDuty notification in OCI Monitoring.
Understand OCI Monitoring: Involves events, rules, and actions for notifications.
Evaluate Options:
A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.
B: Rule—defines conditions for triggering actions—precedes the action.
C: Function—serverless code, not directly tied to notifications.
D: Event—state change triggering a rule, not the notification itself.
Reasoning: The notification is the action taken after an event/rule—fits A.
Conclusion: A is correct.
OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.
1: Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".
問題 #103
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Predict AutoML’s output when time runs out during tuning.
Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
Evaluate Options:
A: Best-known config—Logical, reflects optimization goal—correct.
B: Last config—Ignores prior better results—incorrect.
C: Minimum learning rate—Arbitrary, not performance-based.
D: Random—Defeats tuning purpose.
Reasoning: AutoML prioritizes the best config found within the budget.
Conclusion: A is correct.
OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.
1: Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".
問題 #104
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?
答案：A,D
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI Language AI capabilities.
Understand OCI Language: Focuses on text analysis tasks.
Evaluate Options:
A: Table extraction—Vision, not Language—incorrect.
B: Punctuation correction—Not offered—incorrect.
C: Sentence diagramming—Not supported—incorrect.
D: Topic classification—Supported (custom/pretrained)—correct.
E: Sentiment analysis—Supported (pretrained)—correct.
Reasoning: D and E are core text analysis features of OCI Language.
Conclusion: D and E are correct.
OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
問題 #105
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%
答案：D
解題說明：
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".
問題 #106
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?
答案：A
解題說明：
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".
問題 #107
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