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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q55-Q60):

NEW QUESTION # 55
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

A. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
B. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
C. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
D. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

NEW QUESTION # 56
Where are OCI secrets stored?

A. OCI Vault
B. Oracle Databases
C. Autonomous Data Warehouse
D. OCI Object Storage

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.
* Evaluate Options:
* A: Object Storage is for general data, not secure secret management.
* B: Vault is OCI's service for storing and managing secrets securely.
* C: Autonomous Data Warehouse is for analytics, not secret storage.
* D: Oracle Databases store data, not OCI-specific secrets.
* Reasoning: Vault is purpose-built for secrets with encryption and access control.
* Conclusion: B is correct.
OCI Vault "provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies." Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes-only Vault (B) is designed for secrets per OCI's security architecture.
Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

NEW QUESTION # 57
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?

A. Define the inference server dependencies
B. Define the compute scaling strategy
C. Execute the inference logic code
D. Configure the deployment infrastructure

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define the role of score.py in OCI model artifacts.
* Understand Artifacts: score.py is key for deployment runtime.
* Evaluate Options:
* A: Infra config-Handled by OCI settings, not score.py.
* B: Inference logic-Correct; runs load_model(), predict().
* C: Scaling-Set in deployment, not score.py.
* D: Dependencies-In runtime.yaml, not score.py.
* Reasoning: B aligns with score.py's execution role.
* Conclusion: B is correct.
OCI documentation states: "score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint." A, C, and D are managed elsewhere-only B matches OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

NEW QUESTION # 58
Which step is unique to MLOps, as opposed to DevOps?

A. Continuous training
B. Continuous integration
C. Continuous deployment
D. Continuous delivery

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step unique to MLOps vs. DevOps.
* Compare MLOps and DevOps:
* DevOps: Focuses on software deployment (CI/CD).
* MLOps: Extends DevOps to ML, adding model-specific steps.
* Evaluate Options:
* A: Continuous deployment-Common to both (software/models).
* B: Continuous integration-Common to both (code merging).
* C: Continuous delivery-Common to both (releasing updates).
* D: Continuous training-Unique to MLOps (retraining models with new data).
* Reasoning: Only D addresses ML-specific needs (model retraining).
* Conclusion: D is correct.
OCI documentation notes: "MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance." CI (B), CD (A), and delivery (C) are shared with DevOps-only continuous training (D) is MLOps-specific.
Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

NEW QUESTION # 59
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?

A. The last generated hyperparameter configuration
B. A random hyperparameter configuration
C. A hyperparameter configuration with a minimum learning rate
D. The current best-known hyperparameter configuration

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Predict AutoML's output when time runs out during tuning.
* Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
* Evaluate Options:
* A: Best-known config-Logical, reflects optimization goal-correct.
* B: Last config-Ignores prior better results-incorrect.
* C: Minimum learning rate-Arbitrary, not performance-based.
* D: Random-Defeats tuning purpose.
* Reasoning: AutoML prioritizes the best config found within the budget.
* Conclusion: A is correct.
OCI AutoML documentation states: "If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics." Last (B), minimum (C), or random (D) configs aren't selected-only A aligns with OCI's optimization strategy.
Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

NEW QUESTION # 60
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