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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science Service | 30% | - Projects and notebooks
|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
| Model Development and Deployment | 30% | - Model deployment
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
現代生活の速いペースの途方もないストレスの下で、1Z0-1110-26証明書を学ぶことに固執することは、競争力のある人間として自分を証明するために必要になります。 1Z0-1110-26練習問題は、最も有用な試験サポート資料として一般的に知られており、グローバルなインターネットストアフロントから入手できます。長年の努力の末、当社の1Z0-1110-26試験の資料とサービスは、膨大な数のお客様から評価と称賛を受けました。ますます多くの受験者が試験計画ユーティリティとして1Z0-1110-26学習教材を選択します。
質問 # 76
What is the correct definition of Git?
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
Define Git: Git is a version control system—centralized vs. distributed is key.
Evaluate Options:
A: Incorrect—Git is distributed, not centralized (e.g., SVN is centralized).
B: Correct—Distributed, tracks file changes across local and remote repos.
C: Incorrect—Git allows simultaneous contributions; it manages, not prevents, merges.
D: Incorrect—Centralized is wrong, and “copious data” is vague.
Reasoning: Git’s distributed nature (each user has a full repo copy) and change-tracking are core traits.
Conclusion: B is accurate.
OCI documentation aligns with Git’s official definition: “Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management.” A and D misclassify it as centralized, while C misrepresents merge handling—B captures Git’s essence as used in OCI Data Science.
1: Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".
質問 # 77
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
正解：B、D
解説：
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
質問 # 78
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?
正解：B
解説：
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".
質問 # 79
What is the primary difference between a data scientist and a data engineer?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Differentiate data scientist vs. data engineer roles.
Define Roles:
Data Engineer: Builds pipelines, prepares data.
Data Scientist: Analyzes data, builds models.
Evaluate Options:
A: Engineer preps, scientist analyzes—Correct division.
B: Reverses roles—Incorrect.
C: Overlaps roles—Scientist doesn’t typically build pipelines.
D: Misaligns—Analyst isn’t the focus.
Reasoning: A reflects standard role separation.
Conclusion: A is correct.
OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus—only A is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".
質問 # 80
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service to sort images by language.
Evaluate Options:
A: Digital Assistant—Chatbots, not image/language processing.
B: Vision—Image analysis (e.g., object detection), not language sorting.
C: Speech—Audio-to-text, not image-based.
D: Language—Text analysis (e.g., language detection) after OCR—correct.
Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.
Conclusion: D is correct.
OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection".
質問 # 81
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