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| Section | Weight | Objectives |
|---|---|---|
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model development, training, and evaluation - Data preparation, exploration, and transformation |
| Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration |
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
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質問 # 46
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 # 47
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Scale up notebook without losing work.
Evaluate Options:
A: Object Storage—Extra steps, inefficient.
B: Block volume—Persists data, seamless scale—correct.
C: Local machine—Risky, cumbersome.
D: Recreate—Loses work, impractical.
Reasoning: B uses OCI’s built-in persistence.
Conclusion: B is correct.
OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
質問 # 48
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS-supported data formats?
正解：C、D、E
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data formats supported by ADS SDK for loading data.
Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
Evaluate Options:
A . DOCX: Not natively supported—requires conversion (e.g., to text).
B . Pandas DataFrame: Supported—core format for data manipulation in ADS.
C . JSON: Supported—common structured data format.
D . Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).
E . XML: Supported—parseable structured format.
Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.
Conclusion: B, C, E are correct.
OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".
質問 # 49
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Understand parallel execution in OCI Pipelines.
Evaluate Options:
A: False—Pipelines support parallelism.
B: True—DAG allows sequential/parallel steps—correct.
C: False—Not all steps must be parallel.
D: False—Independence enables parallelism.
Reasoning: B reflects OCI’s DAG-based flexibility.
Conclusion: B is correct.
OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".
質問 # 50
The feature type TechJob has the following registered validators:
TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_default_handler) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_open_handler, condition=('job_family',)) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_closed_handler, condition=('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
Understand Validator System: Likely ADS SDK—executes handlers based on conditions.
Analyze Validators:
Default: is_tech_job_default_handler—No condition, fallback.
Open: is_tech_job_open_handler—Condition ('job_family',)—requires job_family arg.
Closed: is_tech_job_closed_handler—Condition ('job_family': 'IT')—requires job_family='IT'.
Evaluate Call: job_family='Engineering'—Matches job_family presence, not IT.
Reasoning:
Open handler applies (tuple condition means arg exists).
Closed fails (Engineering ≠ IT).
Default is overridden by specific matches.
Conclusion: D is correct.
OCI ADS documentation states: “Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT—default (A) is bypassed, no error (B).” Only D fits per ADS validator logic.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".
質問 # 51
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