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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.
Topic 2	Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.
Topic 3	OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.
Topic 4	Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.
Topic 5	Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q130-Q135):

NEW QUESTION # 130
Which architecture is based on the principle of "never trust, always verify"?

A. Fluid perimeter
B. Zero trust
C. Federated identity
D. Defense in depth

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the architecture with "never trust, always verify."
* Evaluate Options:
* A: Federated identity-Shares auth, not verification-focused.
* B: Zero trust-Explicitly "never trust, always verify"-correct.
* C: Fluid perimeter-Adaptive, not the core principle.
* D: Defense in depth-Layered, not verification-centric.
* Reasoning: Zero trust matches the stated principle exactly.
* Conclusion: B is correct.
OCI documentation states: "Zero trust (B) architecture operates on 'never trust, always verify,' requiring continuous authentication and authorization." A, C, and D have different focuses-only B aligns with OCI's security philosophy.
Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".

NEW QUESTION # 131
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?

A. Vault
B. Data Safe
C. Cloud Guard
D. Data Guard

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for key and secret management.
* Evaluate Options:
* A: Data Safe-Database security, not key management.
* B: Cloud Guard-Threat detection, not keys.
* C: Data Guard-DB replication, not keys.
* D: Vault-Key and secret management-correct.
* Reasoning: Vault is OCI's dedicated service for crypto keys and secrets.
* Conclusion: D is correct.
OCI documentation states: "OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access." A, B, and C serve other purposes-only D matches per OCI's securityservices.
Oracle Cloud Infrastructure Vault Documentation, "Overview".

NEW QUESTION # 132
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?

A. Use environmental variables
B. Configure a compute shape
C. Use command-line variables
D. Configure a block volume

Answer: A

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 # 133
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?

A. model_artifact_validate.py
B. requirements.txt
C. score.py
D. runtime.yaml

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Specify third-party libraries for model deployment.
* Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
* Evaluate Options:
* A: Not a standard file-incorrect.
* B: Inference code-not for dependencies.
* C: Defines conda env with dependencies-correct.
* D: Pip list-not used in OCI conda deployments.
* Reasoning: runtime.yaml points to a conda env with all libraries.
* Conclusion: C is correct.
OCI documentation states: "In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model." score.py (B) is for logic, requirements.txt (D) isn't OCI-standard, and A doesn't exist-C fixes the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

NEW QUESTION # 134
In machine learning, what is the primary difference between supervised and unsupervised learning?

A. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.
B. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
C. Supervised learning is created and managed by the Data Engineer.
D. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the key difference between supervised and unsupervised learning.
* Define Types:
* Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
* Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
* Evaluate Options:
* A: Labeled vs. unlabeled-Core distinction, correct.
* B: Monitoring-Misleading, not the primary difference.
* C: Image recognition-False, supervised applies broadly.
* D: Data Engineer-Irrelevant to learning type.
* Reasoning: A captures the foundational data difference.
* Conclusion: A is correct.
OCI documentation states: "Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns." B, C, and D misrepresent this-only A aligns with OCI's ML definitions and industry standards.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

NEW QUESTION # 135
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