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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Topic 2: Apply MLOps Practices | - Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models |
| Topic 3: 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 |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Prepare and manage datasets - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines |
| Topic 5: OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture |
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問題 #94
Which THREE types of data are used for Data Labeling?
答案：B,C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).
Understand Data Labeling: Annotates data for ML—focuses on specific types.
Evaluate Options (Assuming Typical Set):
A: Audio—Not supported—incorrect.
B: Text Document—Supported (e.g., NER)—correct.
C: Images—Supported (e.g., object detection)—correct.
D: Graphs—Not a standard type—incorrect.
Assumed E: Videos—Supported but missing—adjust to fit.
Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.
Conclusion: B, C (third likely video, missing).
OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
問題 #95
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the dat a. Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Get summary info from an ADSDataset object.
Evaluate Options:
A: Correlation matrix—Specific, not full summary.
B: Converts to XGBoost—Not for summary.
C: Executes computation—Not summary-focused.
D: Displays summary (types, counts, dist)—correct.
Reasoning: show_in_notebook() provides a comprehensive overview.
Conclusion: D is correct.
OCI documentation states: “show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook.” A is partial, B and C are unrelated—only D meets the need per ADS SDK.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".
問題 #96
Which technique can be used for feature engineering in the machine learning lifecycle?
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
問題 #97
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.
答案：A
解題說明：
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").
問題 #98
Triggering a PagerDuty notification as part of Monitoring is an example of what in the OCI Console?
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Classify a PagerDuty notification in OCI Monitoring.
Understand OCI Monitoring: Involves events, rules, and actions for notifications.
Evaluate Options:
A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.
B: Rule—defines conditions for triggering actions—precedes the action.
C: Function—serverless code, not directly tied to notifications.
D: Event—state change triggering a rule, not the notification itself.
Reasoning: The notification is the action taken after an event/rule—fits A.
Conclusion: A is correct.
OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.
1: Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".
問題 #99
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