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| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation - Deploy models and manage endpoints - Data preparation, exploration, and transformation |
| 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 |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking |
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NEW QUESTION # 102
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: B
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 # 103
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently increase storage for an OCI Job.
Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
Evaluate Options:
A: False—Jobs can’t edit storage post-creation; it’s fixed.
B: False—No environment variable adjusts storage size.
C: True—Create a new job with larger storage (e.g., 200 GB) and run it.
D: False—Refactoring code is inefficient compared to increasing storage.
Reasoning: C is the standard OCI process for adjusting resources.
Conclusion: C is correct.
OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".
NEW QUESTION # 104
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the interactive coding environment in OCI Data Science.
Evaluate Options:
A: Model Catalog stores models—not for coding.
B: Jobs run predefined tasks—not interactive.
C: Notebook Sessions provide JupyterLab for coding and training—interactive.
D: Projects organize work—not a coding environment.
Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.
Conclusion: C is correct.
OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 105
What happens when a notebook session is deactivated?
Answer: D
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 # 106
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?
Answer: A
Explanation:
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".
NEW QUESTION # 107
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