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| Section | Objectives |
|---|---|
| Topic 1: Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Topic 2: OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources |
| Topic 3: Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Prepare and manage datasets - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines |
| Topic 4: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| Topic 5: 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 |
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NEW QUESTION # 21
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets from reference libraries and index websites such as scikit-learn?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for dataset access (e.g., scikit-learn).
Evaluate Options:
A: DataLabeling—Not an ADS class.
B: DatasetBrowser—Not real.
C: SecretKeeper—Credentials, not data.
D: DatasetFactory—Loads datasets (e.g., open())—correct.
Reasoning: DatasetFactory simplifies library dataset access.
Conclusion: D is correct.
OCI documentation states: “DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit-learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A, B, and C don’t exist or apply—only D fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 22
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Prepare Model: Build and train the time series model using historical data.
Verify: Validate the model’s accuracy (e.g., using metrics like MAE or RMSE).
Save: Store the trained model (e.g., in the OCI Model Catalog).
Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
Predict: Generate revenue forecasts for the upcoming quarter.
Evaluate Options: D follows this logical flow; others (e.g., A starts with “verify” before preparation) don’t.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 23
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find optimal compute shape balancing cost and time.
Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
Evaluate Options:
A: Tuning parameters when underutilized—focuses on model, not shape optimization.
B: Strongest shape—Costly, ignores balance; overkill likely.
C: Scale up from small shape when fully utilized—Balances cost/time effectively.
D: Random start with pre-tests—Unsystematic and inefficient.
Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
Conclusion: C is correct.
OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus—balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure—only C aligns with this method.
1: Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".
NEW QUESTION # 24
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Jobs logging.
Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
Evaluate Options:
A: False—Each run has its own log, not a single job log.
B: False—Logging is optional, not mandatory.
C: True—When enabled, stdout/stderr are auto-captured.
D: False—Logs persist unless explicitly deleted.
Reasoning: C matches OCI’s automatic logging feature.
Conclusion: C is correct.
OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".
NEW QUESTION # 25
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose the better model for cancer prediction based on metrics.
Understand Metrics:
Accuracy: Overall correct predictions.
Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).
Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.
Evaluate Models:
Model 1: 80% accuracy, 70% recall—Misses more cancers.
Model 2: 75% accuracy, 85% recall—Misses fewer cancers.
Evaluate Options:
A: High recall—True, but lacks context.
B: High accuracy—Misses recall’s importance.
C: Recall’s impact—Correct for cancer use case—best.
D: Lesser recall impact—Incorrect for this priority.
Reasoning: C emphasizes recall’s critical role—aligns with medical needs.
Conclusion: C is correct.
OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.
1: Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".
NEW QUESTION # 26
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