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| Section | Weight | Objectives |
|---|---|---|
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration |
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| 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 - Data preparation, exploration, and transformation - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Model development, training, and evaluation |
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NEW QUESTION # 90
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: C
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 # 91
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML. You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence OCI AutoML pipeline stages.
Stages:
Adaptive sampling: Reduces data size if large.
Feature selection: Picks relevant features.
Algorithm selection: Chooses best model type.
Hyperparameter tuning: Optimizes model params.
Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.
Reasoning: Sampling precedes feature work—standard in OCI.
Conclusion: C is correct.
OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".
NEW QUESTION # 92
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials needed.
Analyze 404 Error: Indicates an authorization failure—likely missing permissions or misconfigured resource principal.
Evaluate Options:
A: True—Dynamic group must include notebook sessions (e.g., resource.type = 'datasciencenotebooksession') to authenticate.
B: False—Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
C: True—Policy must grant manage data-science-models to the dynamic group for catalog access.
D: False—Service gateway ensures network access, but 404 is auth-related, not connectivity.
E: False—Resource principal uses dynamic group policies, not user group policies.
Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth—others are tangential.
Conclusion: A and C are correct.
OCI documentation states: “To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = 'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.” B is unrelated (storage location), D is network-focused, and E applies to user auth—not resource principal. A 404 error flags missing auth, fixed by A and C.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".
NEW QUESTION # 93
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Transcribe Spanish video audio and identify profanity.
Evaluate Options:
A: Anomaly Detection—Not for transcription or text analysis.
B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.
C: Translation—Translates text, not transcription.
D: Language—Analyzes text (e.g., profanity), but needs transcribed input.
Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
Conclusion: B is correct for transcription.
OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.
1: Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
NEW QUESTION # 94
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. How does the required False Alarm Probability setting affect an anomaly detection model?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
Understand FAP: Controls false positive rate—threshold for anomaly flagging.
Evaluate Options:
A: Disable reporting—Incorrect; FAP sets sensitivity, not on/off.
B: Changes sensitivity—Correct; lower FAP = fewer false positives—correct.
C: Count-based error—Incorrect; not a counter.
D: Score per signal—Incorrect; FAP is a global setting.
Reasoning: FAP adjusts detection threshold—direct impact on sensitivity.
Conclusion: B is correct.
OCI documentation states: “False Alarm Probability (FAP) (B) adjusts the model’s sensitivity in Anomaly Detection—lower values increase specificity, reducing false positives.” A, C, and D misinterpret FAP’s role—only B aligns with OCI’s anomaly detection tuning.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".
NEW QUESTION # 95
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