Reasoning: MLlib is Spark’s official ML toolkit (e.g., regression, clustering). Conclusion: A is correct (noting “MLib” should be “MLlib”). OCI Data Science supports Spark via Data Flow, where “MLlib (Machine Learning library) provides scalable ML algorithms.” GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn’t real —MLlib (A) is the standard, despite the typo. : Oracle Cloud Infrastructure Data Flow Documentation, "Apache Spark MLlib". You are a researcher who requires access to large datasets. Which OCI service would you use?
Objective: Identify the OCI service for scalable Spark applications. Evaluate Options: A: Data Science —ML platform, not Spark -focused. B: Anomaly Detection —Specific ML service, not general Spark. C: Data Labeling —Annotation tool, not Spark -related. D: Data Flow —Managed Spark service for big data. Reasoning: Data Flow is OCI’s Spark execution engine. Conclusion: D is correct. OCI Data Flow “provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks.” Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution —B and C are unrelated. : Oracle Cloud Infrastructure Data Flow Documentation, "Overview". Where do calls to stdout and stderr from score.py go in the model deployment?
C. Create a new job with increased storage size and then run the job D. Your code using too much disk space. Refactor the code to identify the problem Explanation: Detailed Answer in Step -by-Step Solution: Objective: Efficiently increase storage for an OCI Job. Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable. Evaluate Options: A: False —Jobs can’t edit storage post-creation; it’s fixed. B: False —No environment variable adjusts storage size. C: True —Create a new job with larger storage (e.g., 200 GB) and run it. D: False —Refactoring code is inefficient compared to increasing storage. Reasoning: C is the standard OCI process for adjusting resources. Conclusion: C is correct. OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue — only C is efficient. : Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration". After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
Reasoning: C (preinstalled) and D (installable) are practical access points. Conclusion: C and D are correct. OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle -ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS —only C and D apply. : Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation". You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?
and E applies to user auth —not resource principal. A 404 error flags missing auth, fixed by A and C. : Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK". You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?
➢ 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?
