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NEW QUESTION # 62
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?
Answer: C
Explanation:
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".
NEW QUESTION # 63
You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
* Identify Shareable Artifacts: When sharing, all components-model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)-are accessible.
* Evaluate Options:
* A: Excludes the model itself-incorrect.
* B: Excludes metrics-incorrect.
* C: Excludes metadata and hyperparameters-incorrect.
* D: Includes all components-correct.
* Conclusion: D is comprehensive and accurate.
The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").
NEW QUESTION # 64
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid OCI IAM policy.
* Understand Policies: Use verbs (e.g., use, manage) and resource types.
* Evaluate Options:
* A: Valid-virtual-network-family is a resource type.
* B: Invalid-No data-science-model-sessions resource type; likely meant data-science-models.
* C: Valid-data-science-projects is correct.
* D: Valid-data-science-family aggregates Data Science resources.
* Reasoning: B uses a nonexistent resource type-error in syntax.
* Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: "data-science-models, data-science-projects, data-science- family-but not data-science-model-sessions (B)." A, C, D are correct-B is invalid due to this typo
/misunderstanding.
Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".
NEW QUESTION # 65
Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify protection for data at rest/transit with customer control.
* Evaluate Options:
* A: Controls-Broad, not specific to encryption.
* B: Isolation-Separates tenants, not crypto-focused.
* C: Encryption-Secures data, allows key management-correct.
* D: Federation-Auth sharing, not data protection.
* Reasoning: C provides crypto control (e.g., Vault keys).
* Conclusion: C is correct.
OCI documentation states: "Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs." A and B are broader, D is unrelated-only C fits per OCI's security model.
Oracle Cloud Infrastructure Security Documentation, "Data Encryption".
NEW QUESTION # 66
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the interactive coding environment in OCI Data Science.
* Evaluate Options:
* A: Model Catalog stores models-not for coding.
* B: Jobs run predefined tasks-not interactive.
* C: Notebook Sessions provide JupyterLab for coding and training-interactive.
* D: Projects organize work-not a coding environment.
* Reasoning: Notebook Sessions are OCI's Jupyter-based tool for interactive ML development.
* Conclusion: C is correct.
OCI Data Science Notebook Sessions "provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models." Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization-only C offers interactivity.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 67
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