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. : Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types". Which of the following analytical and statistical techniques do data scientists commonly use?
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. : Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages". True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
Data Engineer: Builds pipelines, prepares data. Data Scientist: Analyzes data, builds models. Evaluate Options: A: Engineer preps, scientist analyzes —Correct division. B: Reverses roles —Incorrect. C: Overlaps roles —Scientist doesn’t typically build pipelines. D: Misaligns —Analyst isn’t the focus. Reasoning: A reflects standard role separation. Conclusion: A is correct. OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus —only A is accurate. : Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science". What is the first step in the data science process?
Explanation: Detailed Answer in Step -by-Step Solution: Objective: Define data science’s main goal. Evaluate Options: A: Archiving —Not the focus; too narrow. B: Analyze for insights/business value —Core purpose —correct. C: Prep for analytics —Means, not the end goal. D: Output -focused —Vague, incomplete. Reasoning: B captures the actionable insight generation central to data science. Conclusion: B is correct. OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage -focused, C is preparatory, and D is unclear —only B reflects the principal goal per OCI’s mission. : Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?". You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?
➢ TOTAL QUESTIONS: 308 A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
