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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Model Development and Deployment | 30% | - Model deployment
|
| OCI Data Science Service | 30% | - Model catalog
|
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NEW QUESTION # 144
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?
Answer: C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Publish a conda env to Object Storage.
Process: Initialize bucket config, then publish env.
Evaluate Options:
A: Publishes env with slug—correct final step.
B: Lists envs—unrelated to publishing.
C: Sets bucket details—required setup—correct.
D: Creates env—not publishing.
E: Activates env—not for sharing.
Reasoning: C sets up, A executes—standard workflow.
Conclusion: A and C are correct.
OCI documentation states: “To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.” B, D, and E serve other purposes—only A and C are required per OCI’s process.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".
NEW QUESTION # 145
What is the first step in the data science process?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the initial data science step.
Define Process: Starts with problem definition, then data and modeling.
Evaluate Options:
A: Data collection—Second step after problem definition.
B: Modeling—Later stage.
C: Hypothesis—Sets the goal, first step—correct.
D: Data owners—Collaboration, not the start.
Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).
Conclusion: C is correct.
OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".
NEW QUESTION # 146
Which step is a part of the AutoML pipeline?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
NEW QUESTION # 147
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Get all AutoML trial results.
Understand AutoML: Trials include hyperparameter tuning outcomes.
Evaluate Options:
A: print_trials()—Displays all trial results—correct.
B: visualize_tuning_trials()—Visualizes tuning, not full list.
C: visualize_adaptive_sampling_trials()—Specific to sampling, not all trials.
D: visualize_algorithm_selection_trials()—Specific to algorithms, not all.
Reasoning: A provides comprehensive trial output.
Conclusion: A is correct.
OCI AutoML documentation states: “print_trials() outputs a table of all trials performed, including hyperparameters and scores.” Visualization methods (B, C, D) focus on specific aspects—only A gives the full list.
1: Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".
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
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