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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q147-Q152):
NEW QUESTION # 147
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
* Evaluate Options:
* A: DatasetBrowser-Not an ADS class.
* B: DatasetFactory-Loads datasets from sources like scikit-learn-correct.
* C: ADSTuner-Hyperparameter tuning, not data access.
* D: SecretKeeper-Manages credentials, not datasets.
* Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
* Conclusion: B is correct.
OCI documentation states: "DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris'))." A isn't real, C tunes models, and D handles secrets-only B fits.
Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 148
You want to make your model more parsimonious to reduce the cost of collecting and processing data. 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: A
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.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
NEW QUESTION # 149
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels.
Which types of custom models can be trained?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language service.
* Understand OCI Language: Focuses on text analysis, not images.
* Evaluate Options:
* A: Image classification-Incorrect; Language is text-based.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment-Pretrained, not custom; NER ok-incorrect.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's custom text capabilities.
* Conclusion: B is correct.
OCI documentation states: "OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data." A and D involve images, C includes pretrained sentiment- only B matches OCI Language's custom model scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
NEW QUESTION # 150
Which CLI command allows the customized conda environment to be shared with co-workers?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Share a custom conda environment in OCI Data Science.
* Understand Commands: OCI provides odsc CLI for environment management.
* Evaluate Options:
* A: clone duplicates an environment locally-not for sharing.
* B: publish uploads the environment to Object Storage for team access-correct.
* C: modify doesn't exist as a standard command.
* D: install sets up an environment locally-not for sharing.
* Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
* Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: "Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users." clone (A) is for local duplication, modify (C) isn't valid, and install (D) is for local setup-not sharing. B is the designated sharing mechanism.
Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
NEW QUESTION # 151
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for PySpark app development.
* Steps:
* Launch notebook: First-sets up environment.
* Install PySpark conda: Second-adds Spark libraries.
* Configure core-site.xml: Third-connects to data.
* Develop app: Fourth-writes code.
* Data Flow: Fifth-scales (optional).
* Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
* Reasoning: Notebook first, then setup and coding.
* Conclusion: D is correct.
OCI documentation states: "1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core- site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D)." Other orders (A, B, C) misplace notebook launch or config-D is correct.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".
NEW QUESTION # 152
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