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?
E. You can install private or custom libraries from your own internal repositories Explanation: Detailed Answer in Step -by-Step Solution: Objective: Identify correct statements about installing Python libraries in OCI Data Science. Understand Notebook Sessions: Run in a managed environment with specific permissions. Evaluate Options: A: False —No root privileges; users operate as datascience with limited sudo. B: True —pip install from PyPI works with internet access (e.g., NAT Gateway). C: False —Yum isn’t available; pip is the primary tool as a normal user. D: False —Misstated; youcaninstall non-preinstalled libraries —likely a typo (intended opposite). E: True —Custom repos are supported with proper network config. Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true. Conclusion: B, D (corrected), E are correct. OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted —users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability —corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility. : Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions". You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?
OCI documentation states: “For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel.” B misinterprets limits, C wastes time, and D denies capability —only A fits OCI’s design. : Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs". You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
Explanation: Detailed Answer in Step -by-Step Solution: Objective: Sequence steps for a PySpark app in OCI Data Science. Evaluate Steps: Launch notebook: First—provides the environment. Install PySpark conda: Second —sets up Spark libraries. Configure core-site.xml: Third —connects to data (e.g., Object Storage). Develop app: Fourth —writes the PySpark code. Data Flow: Fifth —optional scaling, post-development. Check Options: D (1, 2, 3, 4, 5) matches this logical flow. Reasoning: Notebook first, then setup, coding, and scaling. Conclusion: D is correct. OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this —others (A, B, C) misorder critical steps like launching the notebook. : Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks". You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?
