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. : Oracle Cloud Infrastructure Language Documentation, "Language Detection". You are asked to prepare data for a custom -built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
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". You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions". The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
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?
➢ 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?
