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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Development and Deployment | 30% | - Model training
|
| Topic 2: OCI Data Science Service | 30% | - Model catalog
|
| Topic 3: MLOps and OCI Integration | 20% | - OCI ecosystem
|
| Topic 4: Machine Learning Fundamentals | 20% | - Unsupervised learning
|
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NEW QUESTION # 145
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: A
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 # 146
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
Answer: B,C,F
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify supported annotation classes in OCI Data Labeling.
Understand Tool: Supports image/text annotations for ML.
Evaluate Options:
A: Object detection—Yes (bounding boxes).
B: Named entity—Text-specific, not primary for images.
C: Classification—Yes (labels for images/text).
D: Key-point—Not listed in OCI docs.
E: Polygonal—Not explicitly supported.
F: Semantic segmentation—Yes (pixel-level).
Reasoning: A, C, F match OCI’s image/text focus.
Conclusion: A, C, F are correct.
OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".
NEW QUESTION # 147
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 # 148
Which statement about Oracle Cloud Infrastructure Multi-Factor Authentication (MFA) is NOT valid?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the invalid MFA statement.
Evaluate Options:
A: True—Users can’t disable MFA; admin-controlled.
B: False—Multiple devices can be registered—invalid.
C: True—Authenticator app is required.
D: True—Admins can disable MFA.
Reasoning: B contradicts OCI’s multi-device support.
Conclusion: B is incorrect.
OCI documentation states: “Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)—admins can (D).” Only B is not valid per OCI’s IAM MFA policy.
1: Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".
NEW QUESTION # 149
You want to make your model more parsimonious 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 would be appropriate to display the correlation between Continuous and Categorical features?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features using ADS SDK.
Understand Correlation Types:
Continuous vs. Continuous: Pearson correlation.
Categorical vs. Categorical: Cramer’s V.
Continuous vs. Categorical: Correlation ratio (eta).
Evaluate Options:
A . corr(): General correlation (Pearson), not suited for mixed types—incorrect.
B . correlation_ratio_plot(): Plots correlation ratio for continuous-categorical—correct.
C . pearson_plot(): Not an ADS method; Pearson is continuous-only—incorrect.
D . cramersv_plot(): Cramer’s V for categorical-categorical—incorrect.
Reasoning: Correlation ratio measures association between continuous and categorical variables—ideal for heatmap in this mixed scenario.
Conclusion: B is correct.
OCI documentation states: “The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal.” corr() (A) defaults to Pearson, pearson_plot() (C) isn’t real, and cramersv_plot() (D) is for categorical pairs—only B aligns with OCI’s ADS capabilities for this use case.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
NEW QUESTION # 150
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