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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation - Data preparation, exploration, and transformation |
| Topic 3: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| Topic 4: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
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NEW QUESTION # 143
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Specify third-party libraries for model deployment.
Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
Evaluate Options:
A: Not a standard file—incorrect.
B: Inference code—not for dependencies.
C: Defines conda env with dependencies—correct.
D: Pip list—not used in OCI conda deployments.
Reasoning: runtime.yaml points to a conda env with all libraries.
Conclusion: C is correct.
OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".
NEW QUESTION # 144
Which activity is NOT a part of the machine learning life cycle?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn’t part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.
C: Modeling (e.g., training) is the core of ML—correctly included.
D: Data Access (e.g., retrieving data) is the first ML step—correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
NEW QUESTION # 145
You are running a pipeline in the OCI Data Science service and want to override some of the pipeline's default settings. Which of the following statements about overriding pipeline defaults is true?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.
Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.
Evaluate Options:
A: False—Overrides can occur post-creation, before running.
B: False—Any authorized user, not just admins, can override defaults.
C: True—Settings can be adjusted before execution starts.
D: False—Defaults can be changed post-creation, pre-execution.
Conclusion: C is correct as it reflects the flexibility of pipeline configuration.
OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B). (Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").
NEW QUESTION # 146
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine when an OCI Data Science pipeline can start.
Understand Pipelines: They’re workflows with defined steps, executed on demand or scheduled.
Evaluate Options:
A: Once created, a pipeline can be run immediately—correct.
B: “During run state” implies it’s already running—illogical.
C: “After active state” is unclear; pipelines run when triggered, not post-state.
D: “Before active state” is vague—creation precedes running.
Reasoning: Pipelines are executable post-creation via UI/CLI—simplest interpretation is A.
Conclusion: A is correct.
OCI Data Science documentation states: “After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK.” B, C, and D misalign with this—running starts post-creation (A), not during/after ambiguous states.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".
NEW QUESTION # 147
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?
Answer: A,B,D
Explanation:
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".
NEW QUESTION # 148
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