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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 2: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 3: Model Development and Deployment | 30% | - Model deployment
|
| Topic 4: Machine Learning Fundamentals | 20% | - Supervised learning
|
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NEW QUESTION # 149
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for free geospatial images.
Evaluate Options:
A: Big Data Service—Spark processing, not datasets.
B: Analytics Cloud—Visualization, not data source.
C: Data Science—ML platform, not dataset provider.
D: Open Data—Free public datasets, including geospatial—correct.
Reasoning: Open Data is OCI’s public dataset hub.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to curated datasets, including geospatial images, for public use.” A, B, and C serve other purposes—only D delivers free geospatial data.
1: Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".
NEW QUESTION # 150
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an OCI AI service to sort images by language.
Evaluate Options:
A: Digital Assistant—Chatbots, not image/language processing.
B: Vision—Image analysis (e.g., object detection), not language sorting.
C: Speech—Audio-to-text, not image-based.
D: Language—Text analysis (e.g., language detection) after OCR—correct.
Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.
Conclusion: D is correct.
OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection".
NEW QUESTION # 151
Why is data sampling useful for data scientists?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the primary benefit of data sampling.
Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.
Evaluate Options:
A: Small batches reduce resources—True but not the main purpose.
B: Reduces storage—Incidental, not the goal.
C: Representative subset for faster, accurate models—Core purpose of sampling.
Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
Conclusion: C is correct.
OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
NEW QUESTION # 152
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: D
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 # 153
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: C,D
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 # 154
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