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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools |
| Topic 2: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| Topic 3: Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Prepare and manage datasets |
| Topic 5: OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
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問題 #151
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：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently vary hyperparameters in OCI Jobs.
Evaluate Options:
A: New job per run—Wastes setup time.
B: Code changes per job—Inefficient, error-prone.
C: Flexible params per run—Efficient, reusable—correct.
D: New job per run—Redundant effort.
Reasoning: C minimizes job creation, maximizes flexibility.
Conclusion: C is correct.
OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".
問題 #152
Which CLI command allows the customized conda environment to be shared with co-workers?
答案：C
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.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
問題 #153
Which of the following programming languages are most widely used by data scientists?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".
問題 #154
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Get all AutoML trial results.
Understand AutoML: Trials include hyperparameter tuning outcomes.
Evaluate Options:
A: print_trials()—Displays all trial results—correct.
B: visualize_tuning_trials()—Visualizes tuning, not full list.
C: visualize_adaptive_sampling_trials()—Specific to sampling, not all trials.
D: visualize_algorithm_selection_trials()—Specific to algorithms, not all.
Reasoning: A provides comprehensive trial output.
Conclusion: A is correct.
OCI AutoML documentation states: “print_trials() outputs a table of all trials performed, including hyperparameters and scores.” Visualization methods (B, C, D) focus on specific aspects—only A gives the full list.
1: Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".
問題 #155
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?
答案：A,B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify two methods to enhance Object Storage durability.
Understand Durability: Ensures data isn’t lost—focus on redundancy and protection.
Evaluate Options:
A: RAID1—Block volume feature, not Object Storage.
B: Encryption—Secures data, not durability.
C: Versioning—Retains old versions, prevents loss—correct.
D: Limit delete—Prevents accidental deletion—correct.
E: Client encryption—Secures, not durability-focused.
Reasoning: C and D directly protect against data loss—durability-focused.
Conclusion: C and D are correct.
OCI documentation states: “Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss.” A isn’t applicable, B and E focus on security—only C and D enhance durability per OCI’s storage features.
1: Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".
問題 #156
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