Objective: Identify the non -visualized AutoML stage with small data. Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning. Evaluate Options: A: Feature selection —Visualized (e.g., feature importance). B: Algorithm selection —Visualized (e.g., algorithm scores). C: Adaptive sampling —Skipped/visualization absent for <1000 rows. D: Hyperparameter tuning —Visualized (e.g., trial plots). Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals. Conclusion: C is correct. OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals —only C is absent here. : Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages". For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?
Explanation: Detailed Answer in Step -by-Step Solution: Objective: Identify a widely accepted maxim about data scientists’ time allocation. Understand Data Science Workflow: Involves data collection, preparation, and analysis —time distribution is key. Evaluate Options: A: 80% on finding/preparing, 20% analyzing —Reflects the data wrangling challenge. B: 80% analyzing, 20% finding/preparing —Inverts the common perception. C: 80% on failed projects, 20% useful —Pessimistic, not a standard maxim. Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real -world data. Conclusion: A is correct. OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported —only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance. : Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview". Why is data sampling useful for data scientists?
True or false? Bias is a common problem in data science applications.
(Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines"). How are datasets exported in the OCI Data Labeling service?
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.
