Explanation: Detailed Answer in Step -by-Step Solution: Objective: Identify a true statement about OCI Data Science Jobs. Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure. Evaluate Options: A: True —Jobs provision OCI compute resources on -demand for task execution. B: False —Users define custom tasks (e.g., Python scripts), not limited to standard ones. C: False —Infrastructure is fully managed by OCI, not user -managed. D: False —Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file. Reasoning: A reflects OCI’s managed, on -demand provisioning model for Jobs. Conclusion: A is correct. The OCI Data Science documentation states: “Jobs provision compute infrastructure on -demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility —only A accurately describes Jobs. : Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview". Which step is unique to MLOps, as opposed to DevOps?
Explanation: Detailed Answer in Step -by-Step Solution: Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases. Evaluate Statements: A: True —Open Data includes text and image datasets (e.g., geospatial images). B: False —Video and other formats may be available depending on the dataset; no strict exclusion exists. C: False —Datasets may include metadata, but code/tooling examples aren’t guaranteed. D: True —It’s designed for data scientists and analysts who work with datasets. E: False —It’s not a user-contributed repository; it’s curated by Oracle. F: False —Open Data is free and public, not subscription -based. Select Two: A and D align with the service’s purpose and offerings. OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data"). You are running a pipeline in the OCI Data Science service and want to override some of the pipeline's default settings. Which of the following statements about overriding pipeline defaults is true?
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
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?
