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問題 #145
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
答案：D
解題說明：
Detailed Answer in Step-by-Step Solution:
* Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
* Evaluate Components:
* A: User groups are created for role-based access-automated.
* B: Dynamic groups (e.g., for notebook sessions) are included-automated.
* C: Individual users require manual creation via IAM-not automated.
* D: Basic policies (e.g., access to Data Science resources) are included-automated.
* Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
* Conclusion: C is the exception.
The OCI Resource Manager template for Data Science "automatically provisions user groups, dynamic groups, and policies for basic use cases," but "individual users must be created separately in IAM and assigned to groups." C is the only item not handled by the template, per the documentation.
Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".
問題 #146
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
答案：D
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
* Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
* Evaluate Options:
* A: New job per run with env vars-Redundant job creation, inefficient.
* B: New job per run with args-Similarly inefficient due to repeated setup.
* C: Hardcode params, new job per change-Highly inefficient, requires code edits.
* D: Single job, flexible params via env vars or args-Efficient, reusable-correct.
* Reasoning: D minimizes job creation, allows runtime flexibility via configuration-best practice.
* Conclusion: D is correct.
OCI documentation states: "For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK- most efficient approach." Options A, B, and C involve unnecessary job proliferation or code changes-only D aligns with OCI's design for parameterized runs.
Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".
問題 #147
How can you collaborate with team members in OCI Data Science Workspace?
答案：A
解題說明：
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".
問題 #148
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients.
There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
* Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
* Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
* Evaluate Options:
* A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn't typically include readable source code; it's a trained object, not the training script.
* B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
* C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
* D. Check for provenance details: Provenance tracks the model's creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
* Reasoning: Provenance details are designed to document the "how" and "what" of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
* Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes "details about the model's origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references." This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.
問題 #149
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?
答案：A,B,C
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three annotation methods in OCI Data Labeling for images.
* Understand Data Labeling: Supports image annotations for ML.
* Evaluate Options:
* A: Semantic segmentation with boxes-Incorrect; segmentation is pixel-based, not boxes.
* B: Single label (classification)-Supported-correct.
* C: No bounding boxes-False; boxes are supported.
* D: Object detection with boxes-Supported-correct.
* E: Multiple labels (multi-label)-Supported-correct.
* Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
* Conclusion: B, D, E are correct.
OCI documentation states: "Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E)." A misdefines segmentation, C contradicts support-only B, D, E are valid per OCI's Data Labeling features.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
問題 #150
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