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| Section | Objectives |
|---|---|
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
>> Oracle 1Z0-1110-26資格トレーリング <<
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質問 # 19
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?
正解：D
解説：
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
質問 # 20
Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify OCI’s managed Kubernetes service.
Evaluate Options:
A: Container Registry—Stores images, not Kubernetes.
B: Load Balancing—Network service, not Kubernetes.
C: Container Engine (OKE)—Managed Kubernetes—correct.
D: Streaming—Data streaming, not containers.
Reasoning: C is OCI’s Kubernetes offering—OKE.
Conclusion: C is correct.
OCI documentation states: “OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes.” A, B, and D serve other purposes—only C matches per OCI’s container services.
1: Oracle Cloud Infrastructure OKE Documentation, "Overview".
質問 # 21
Which is NOT a compliance document?
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a non-compliance document in OCI context.
Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).
Evaluate Options:
A: Certificate—Proof of compliance (e.g., ISO)—compliance doc.
B: Pen test report—Security test result, not formal compliance—correct.
C: Attestation—Statement of compliance—compliance doc.
D: Bridge letter—Links audit periods—compliance doc.
Reasoning: B is operational, not a compliance artifact.
Conclusion: B is correct.
OCI documentation lists “compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs.” Only B stands apart per OCI’s compliance terminology.
1: Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".
質問 # 22
What is the correct definition of Git?
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
Define Git: Git is a version control system—centralized vs. distributed is key.
Evaluate Options:
A: Incorrect—Git is distributed, not centralized (e.g., SVN is centralized).
B: Correct—Distributed, tracks file changes across local and remote repos.
C: Incorrect—Git allows simultaneous contributions; it manages, not prevents, merges.
D: Incorrect—Centralized is wrong, and “copious data” is vague.
Reasoning: Git’s distributed nature (each user has a full repo copy) and change-tracking are core traits.
Conclusion: B is accurate.
OCI documentation aligns with Git’s official definition: “Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management.” A and D misclassify it as centralized, while C misrepresents merge handling—B captures Git’s essence as used in OCI Data Science.
1: Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".
質問 # 23
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for ML model lifecycle.
Evaluate Options:
A: Data Catalog—Metadata management, not ML.
B: Data Integration—ETL, not ML.
C: Data Science—Full ML lifecycle—correct.
D: Data Flow—Spark processing, not full ML.
Reasoning: C supports building, training, deploying models.
Conclusion: C is correct.
OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.
1: Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".
質問 # 24
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