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Oracle 1z0-1110-25 考試大綱：
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1z0-1110-25最新題庫資源：Oracle Cloud Infrastructure 2025 Data Science Professional考試，Oracle 1z0-1110-25—100％免費
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最新的 Oracle Cloud 1z0-1110-25 免費考試真題 (Q80-Q85):
問題 #80
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：C
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.
Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".
問題 #81
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Transcribe Spanish video audio and identify profanity.
* Evaluate Options:
* A: Anomaly Detection-Not for transcription or text analysis.
* B: Speech-Converts audio to text (e.g., Spanish), base for further analysis-correct.
* C: Translation-Translates text, not transcription.
* D: Language-Analyzes text (e.g., profanity), but needs transcribed input.
* Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
* Conclusion: B is correct for transcription.
OCI Speech "transcribes audio from video or audio files into text, supporting languages like Spanish." Post- transcription, OCI Language could detect profanity, but B is the starting point-Anomaly (A) and Translation (C) don't fit.
Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
問題 #82
Which CLI command allows the customized conda environment to be shared with co-workers?
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Share a custom conda environment in OCI Data Science.
* Understand Commands: OCI provides odsc CLI for environment management.
* Evaluate Options:
* A: clone duplicates an environment locally-not for sharing.
* B: publish uploads the environment to Object Storage for team access-correct.
* C: modify doesn't exist as a standard command.
* D: install sets up an environment locally-not for sharing.
* Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
* Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: "Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users." clone (A) is for local duplication, modify (C) isn't valid, and install (D) is for local setup-not sharing. B is the designated sharing mechanism.
Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
問題 #83
On which option do you set Oracle Cloud Infrastructure Budget?
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
問題 #84
How are datasets exported in the OCI Data Labeling service?
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
* Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
* Evaluate Options:
* A: Binary isn't a standard export format for annotations.
* B: XML isn't used; JSON is preferred for flexibility.
* C: Line-delimited JSON is the correct format, aligning with ML workflows.
* D: CSV is common but not the default for OCI Data Labeling.
* Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation.
(Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
問題 #85
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