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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Prepare and manage datasets - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models |
| Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Apply MLOps Practices | - Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models |
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
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NEW QUESTION # 133
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the ADS SDK class for visualizing model performance comparison.
Understand ADS Classes: Each serves a specific ML purpose—visualization requires evaluation tools.
Evaluate Options:
A . EvaluationMetrics: Likely a typo—meant EvaluationsMetrics? Not a standalone class for visualization.
B . ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)—correct.
C . ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
D . ADSTuner: Tunes hyperparameters, not for visualization.
Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
Conclusion: B is correct.
OCI documentation states: “The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines.” EvaluationMetrics (A) isn’t a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning—only B fits the visualization need per OCI’s ADS toolkit.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".
NEW QUESTION # 134
Which of these options allow the sharing and loading back of ML models into a notebook session?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
Evaluate Options:
A . Model provenance: Tracks model origin—informative but not a sharing mechanism.
B . Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.
C . Model deployment: Makes models accessible as endpoints, not for notebook reloading.
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
Conclusion: D is the correct tool.
The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 135
Which encryption is used for Oracle Data Science?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify encryption standard for OCI Data Science.
Understand OCI Encryption: Applies to data at rest and in transit.
Evaluate Options:
A: AES-256—Industry-standard, OCI default—correct.
B: DES—Outdated, weak—incorrect.
C: TDES—Older, less secure—incorrect.
D: Twofish—Not OCI standard—incorrect.
E: RSA—Asymmetric, not primary for data at rest—incorrect.
Reasoning: AES-256 is OCI’s go-to for Data Science resources.
Conclusion: A is correct.
OCI documentation states: “Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models.” B, C, D, and E are either outdated or not used—only A matches OCI’s encryption policy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
NEW QUESTION # 136
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
Answer: A,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
Evaluate Metrics:
A . Mean squared error: Used for regression, not classification.
B . Explained variance score: Regression metric, not suitable.
C . Recall: Measures true positive rate per class—key for classification.
D . F1-score: Balances precision and recall—widely used in multiclass.
E . R-squared: Regression metric, not applicable.
Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").
NEW QUESTION # 137
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?
Answer: C
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 # 138
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