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You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?
Detailed Answer in Step-by-Step Solution:
Objective: Configure conda env for a pipeline step.
Evaluate Options:
A: Shape---Infra, not env config.
B: Volume---Storage, not env.
C: Command-line---Step args, not env.
D: Env variables---Sets conda path---correct.
Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
Conclusion: D is correct.
OCI documentation states: ''Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.'' A, B, and C address other aspects---only D fits env config.
: Oracle Cloud Infrastructure Data Science Documentation, 'Pipeline Step Configuration'.
How can you collaborate with team members in OCI Data Science Workspace?
Detailed Answer in Step-by-Step Solution:
Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
Evaluate Options:
A: Access control---Possible but not primary collaboration.
B: Version control (e.g., Git)---Standard for code sharing---correct.
C: Shared instance---Not supported; sessions are single-user.
D: Chat/video---Not a feature of OCI Data Science.
Reasoning: B leverages Git for team collaboration---OCI's recommended method.
Conclusion: B is correct.
OCI documentation states: ''Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks.'' A is limited, C isn't feasible, and D isn't available---only B matches OCI's collaboration approach.
: Oracle Cloud Infrastructure Data Science Documentation, 'Collaboration with Git'.
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'.
What is the name of the machine learning library used in Apache Spark?
Detailed Answer in Step-by-Step Solution:
Objective: Identify Apache Spark's ML library.
Understand Spark: A big data framework with specialized libraries.
Evaluate Options:
A: MLib (correctly MLlib)---Spark's machine learning library.
B: GraphX---Graph processing, not ML.
C: Structured Streaming---Streaming data, not ML.
D: HadoopML---Not a Spark library (Hadoop-related).
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 have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the dat
a. Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
Detailed Answer in Step-by-Step Solution:
Objective: Get summary info from an ADSDataset object.
Evaluate Options:
A: Correlation matrix---Specific, not full summary.
B: Converts to XGBoost---Not for summary.
C: Executes computation---Not summary-focused.
D: Displays summary (types, counts, dist)---correct.
Reasoning: show_in_notebook() provides a comprehensive overview.
Conclusion: D is correct.
OCI documentation states: ''show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook.'' A is partial, B and C are unrelated---only D meets the need per ADS SDK.
: Oracle Cloud Infrastructure ADS SDK Documentation, 'ADSDataset Methods'.
