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NEW QUESTION # 11
Which THREE types of data are used for Data Labeling?
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data types for OCI Data Labeling (question likely incomplete-assuming B, C, D, E options).
* Understand Data Labeling: Annotates data for ML-focuses on specific types.
* Evaluate Options (Assuming Typical Set):
* A: Audio-Not supported-incorrect.
* B: Text Document-Supported (e.g., NER)-correct.
* C: Images-Supported (e.g., object detection)-correct.
* D: Graphs-Not a standard type-incorrect.
* Assumed E: Videos-Supported but missing-adjust to fit.
* Reasoning: OCI supports text, images, and videos-question lists only four, so B and C are definite.
* Conclusion: B, C (third likely video, missing).
OCI documentation states: "Data Labeling supports text documents (B), images (C), and videos for annotation-audio (A) and graphs (D) are not included." Question likely meant three from a larger set; B and C are confirmed per OCI's Data Labeling capabilities.
Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
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NEW QUESTION # 12
What is the correct definition of Git?
Answer: A
Explanation:
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.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".
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NEW QUESTION # 13
How can you collaborate with team members in OCI Data Science Workspace?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".
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NEW QUESTION # 14
Which statement about Oracle Cloud Infrastructure Multi-Factor Authentication (MFA) is NOT valid?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the invalid MFA statement.
* Evaluate Options:
* A: True-Users can't disable MFA; admin-controlled.
* B: False-Multiple devices can be registered-invalid.
* C: True-Authenticator app is required.
* D: True-Admins can disable MFA.
* Reasoning: B contradicts OCI's multi-device support.
* Conclusion: B is incorrect.
OCI documentation states: "Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)-admins can (D)." Only B is not valid per OCI's IAM MFA policy.
Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".
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NEW QUESTION # 15
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?
Answer: D,E
Explanation:
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. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
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NEW QUESTION # 16
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