1z0-1110-25 Oracle Cloud Infrastructure 2025 Data Science Professional Free Practice Exam Questions (2025 Updated)

Detailed Answer in Step-by-Step Solution:

Objective: Determine where to specify the conda environment for an OCI model deployment.

Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.

Evaluate Options:

A. score.py: Contains inference logic (e.g., load_model(), predict())—not for environment specs.

B. runtime.yaml: Defines deployment runtime, including conda environment path—correct.

C. requirements.txt: Lists pip dependencies—not used in OCI for conda environments.

D. model_artifact_validate.py: Not a standard artifact; doesn’t exist in OCI deployment.

Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)—failure to set this causes deployment errors.

Conclusion: B is correct.

OCI documentation states: “The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies.” score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn’t valid—only B addresses the conda issue per OCI’s deployment process.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

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".

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".

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".

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".

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.

Oracle Cloud Infrastructure OKE Documentation, "Overview".