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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
| Model Development and Deployment | 30% | - Model deployment
|
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NEW QUESTION # 146
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: C
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 # 147
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Securely authenticate a recurring OCI Job.
Evaluate Options:
A: PAR—Limited duration, insecure for recurring jobs.
B: Resource principal—Secure, managed auth for Jobs—correct.
C: Personal config—Unscalable, security risk.
D: Vault with personal keys—Complex, still uses user creds.
Reasoning: B uses OCI’s native, secure resource principal mechanism.
Conclusion: B is correct.
OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".
NEW QUESTION # 148
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine minimum log storage duration before archiving in Logging Analytics.
Understand Logging Analytics: Logs are active before archival.
Evaluate Options:
A: 60 days—Too long for minimum.
B: 10 days—Too short.
C: 30 days—Standard minimum—correct.
D: 15 days—Not OCI’s default.
Reasoning: 30 days is OCI’s documented minimum active period.
Conclusion: C is correct.
OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 149
You want to make your model more frugal to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features.
Evaluate Options:
A: Pearson—Continuous vs. continuous—incorrect.
B: Cramer’s V—Categorical vs. categorical—incorrect.
C: Correlation ratio—Continuous vs. categorical—correct.
D: General correlation—Not specific to mixed types.
Reasoning: Correlation ratio handles mixed feature types for heatmaps.
Conclusion: C is correct.
OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
NEW QUESTION # 150
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for a PySpark app in OCI Data Science.
Evaluate Steps:
Launch notebook: First—provides the environment.
Install PySpark conda: Second—sets up Spark libraries.
Configure core-site.xml: Third—connects to data (e.g., Object Storage).
Develop app: Fourth—writes the PySpark code.
Data Flow: Fifth—optional scaling, post-development.
Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
Reasoning: Notebook first, then setup, coding, and scaling.
Conclusion: D is correct.
OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".
NEW QUESTION # 151
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