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Where do calls to stdout and stderr from score.py go in the model deployment?
Detailed Answer in Step-by-Step Solution:
Objective: Locate stdout/stderr output from score.py in deployment.
Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
Evaluate Options:
A: False---No VM file is defined by default; logs go to OCI Logging.
B: False---Console displays UI, not raw logs.
C: False---Cloud Shell is a separate tool, not a log destination.
D: True---Predict logs in OCI Logging capture stdout/stderr.
Reasoning: OCI centralizes logs in its Logging service for deployments.
Conclusion: D is correct.
OCI documentation states: ''During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings.'' A, B, and C don't align with this managed logging approach---only D is accurate.
: Oracle Cloud Infrastructure Data Science Documentation, 'Model Deployment - Logging'.
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?
Detailed Answer in Step-by-Step Solution:
Objective: Determine when an OCI Data Science pipeline can start.
Understand Pipelines: They're workflows with defined steps, executed on demand or scheduled.
Evaluate Options:
A: Once created, a pipeline can be run immediately---correct.
B: ''During run state'' implies it's already running---illogical.
C: ''After active state'' is unclear; pipelines run when triggered, not post-state.
D: ''Before active state'' is vague---creation precedes running.
Reasoning: Pipelines are executable post-creation via UI/CLI---simplest interpretation is A.
Conclusion: A is correct.
OCI Data Science documentation states: ''After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK.'' B, C, and D misalign with this---running starts post-creation (A), not during/after ambiguous states.
: Oracle Cloud Infrastructure Data Science Documentation, 'Pipelines - Running a Pipeline'.
On which option do you set Oracle Cloud Infrastructure Budget?
Detailed Answer in Step-by-Step Solution:
Objective: Determine where OCI budgets are set.
Understand Budgets: Track spending across OCI resources.
Evaluate Options:
A: Compartments---Scoped within tenancy, not budget root.
B: Instances---Specific resources, not budget scope.
C: Tags---Filter costs, not budget setting.
D: Tenancy---Top-level scope for budgets---correct.
Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
Conclusion: D is correct.
OCI documentation states: ''Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.'' A, B, and C are sub-elements---only D is the primary scope per OCI's cost management.
: Oracle Cloud Infrastructure Cost Management Documentation, 'Setting Budgets'.
Which activity is NOT a part of the machine learning life cycle?
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn't part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase---correctly included.
C: Modeling (e.g., training) is the core of ML---correctly included.
D: Data Access (e.g., retrieving data) is the first ML step---correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes ''data access, exploration, feature engineering, modeling, deployment, and monitoring,'' per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI's ML pipeline.
: Oracle Cloud Infrastructure Data Science Documentation, 'Machine Learning Lifecycle Overview'.
What is a conda environment?
Detailed Answer in Step-by-Step Solution:
Define Conda: Conda is a widely used tool for managing packages and environments in data science.
Evaluate Options:
A: Partially true---Conda manages dependencies, but it's broader (an environment system).
B: Incorrect---Kernels (e.g., Jupyter) are separate; Conda manages environments.
C: Correct---Conda is an open-source tool for creating isolated environments with specific packages.
D: Incorrect---Not specific to Oracle AI; it's a general tool.
Reasoning: C captures Conda's full scope as an open-source system, beyond just dependency management (A).
Conclusion: C is the most accurate.
OCI documentation describes Conda as ''an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries.'' A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda's official definition and OCI's usage.
: Oracle Cloud Infrastructure Data Science Documentation, 'Conda Environments Overview'.
