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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 2: Model Development and Deployment | 30% | - Model deployment
|
| Topic 3: OCI Data Science Service | 30% | - Model catalog
|
| Topic 4: MLOps and OCI Integration | 20% | - OCI ecosystem
|
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NEW QUESTION # 124
Which of the following programming languages are most widely used by data scientists?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".
NEW QUESTION # 125
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate artifacts for ADS model deployment.
Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
Evaluate Options:
A: Vault—Stores secrets, not models.
B: Depository—Not an OCI term.
C: Model Catalog—Stores models/artifacts for deployment—correct.
D: Artifactory—Not an OCI service.
Reasoning: Model Catalog is OCI’s model repository for ADS.
Conclusion: C is correct.
OCI documentation states: “ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.” Vault (A) is for secrets, B and D aren’t real—only C supports ADS deployment.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
NEW QUESTION # 126
In machine learning, what is the primary difference between supervised and unsupervised learning?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the key difference between supervised and unsupervised learning.
Define Types:
Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
Evaluate Options:
A: Labeled vs. unlabeled—Core distinction, correct.
B: Monitoring—Misleading, not the primary difference.
C: Image recognition—False, supervised applies broadly.
D: Data Engineer—Irrelevant to learning type.
Reasoning: A captures the foundational data difference.
Conclusion: A is correct.
OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".
NEW QUESTION # 127
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Address declining prediction accuracy and verify model performance.
Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.
Evaluate Options:
A . Retrain the model: Uses new data to update the model—fixes accuracy—correct.
B . Validate with recent data: Tests performance but doesn’t fix—diagnostic only.
C . Drift monitoring: Detects data distribution shifts—verifies cause—correct.
D . Redeploy the model: Repeats deployment, doesn’t address root cause.
E . Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.
Conclusion: A and C are correct.
OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 128
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: D
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 # 129
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