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| Section | Objectives |
|---|---|
| Topic 1: Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Topic 2: Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models |
| Topic 3: Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Topic 4: OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture |
| Topic 5: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects |
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NEW QUESTION # 132
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?
Answer: C
Explanation:
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".
NEW QUESTION # 133
What happens when a notebook session is deactivated?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
Deactivation Impact: Deactivating stops the session to save costs.
Evaluate Options:
A: False—Costs decrease as compute stops.
B: False—Boot volume data isn’t preserved; block volume data is.
C: True—The compute instance shuts down, halting billing.
D: False—Block volume persists unless explicitly deleted.
Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
Conclusion: C is correct.
The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".
NEW QUESTION # 134
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Prepare Model: Build and train the time series model using historical data.
Verify: Validate the model’s accuracy (e.g., using metrics like MAE or RMSE).
Save: Store the trained model (e.g., in the OCI Model Catalog).
Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
Predict: Generate revenue forecasts for the upcoming quarter.
Evaluate Options: D follows this logical flow; others (e.g., A starts with “verify” before preparation) don’t.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 135
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Spot the invalid OCI IAM policy.
Understand Policies: Use verbs (e.g., use, manage) and resource types.
Evaluate Options:
A: Valid—virtual-network-family is a resource type.
B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.
C: Valid—data-science-projects is correct.
D: Valid—data-science-family aggregates Data Science resources.
Reasoning: B uses a nonexistent resource type—error in syntax.
Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.
1: Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".
NEW QUESTION # 136
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?
Answer: A,B
Explanation:
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".
NEW QUESTION # 137
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