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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

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 3

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.

Topic 4

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 5

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q135-Q140):

NEW QUESTION # 135
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients.
There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?

A. Check for provenance details
B. Check for metadata tags
C. Check for model taxonomy details
D. Refer to the code inside the model

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
* Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
* Evaluate Options:
* A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn't typically include readable source code; it's a trained object, not the training script.
* B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
* C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
* D. Check for provenance details: Provenance tracks the model's creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
* Reasoning: Provenance details are designed to document the "how" and "what" of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
* Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes "details about the model's origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references." This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.

NEW QUESTION # 136
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.

A. Key rotation allows you to encrypt no more than five keys at a time
B. Key rotation improves encryption efficiency
C. Periodically rotating keys makes it easier to reuse keys
D. Periodically rotating keys limits the amount of data encrypted by one key version
E. Key rotation reduces risk if a key is ever compromised

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two reasons for key rotation in OCI Vault.
* Understand Key Rotation: Enhances security by updating keys.
* Evaluate Options:
* A: Five-key limit-False, no such restriction.
* B: Efficiency-False, not the purpose.
* C: Reuse-False, rotation prevents reuse.
* D: Reduces risk-True, limits exposure-correct.
* E: Limits data-True, reduces breach scope-correct.
* Reasoning: D and E are security-focused-key Vault benefits.
* Conclusion: D and E are correct.
OCI documentation states: "Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security." A, B, and C misrepresent rotation's purpose- only D and E align with OCI's Vault best practices.
Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".

NEW QUESTION # 137
What is the correct definition of Git?

A. Git is a distributed version control system that protects teams from simultaneous repo contributions and merge requests.
B. Git is a distributed version control system that allows you to track changes made to a set of files.
C. Git is a centralized version control system that allows you to revert to previous versions of files as needed.
D. Git is a centralized version control system that allows data scientists and developers to track copious amounts of data.

Answer: B

Explanation:
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.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".

NEW QUESTION # 138
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?

A. Update the deployment to use fewer instances
B. Update the deployment to use a larger virtual machine (more CPUs/memory)
C. Reduce the load balancer bandwidth limit so that fewer requests come in
D. Delete the deployment
E. Update the deployment to add more instances

Answer: B,C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Prevent deployment failure due to utilization limits.
* Understand Utilization: High load requires capacity or throttling.
* Evaluate Options:
* A: More instances-Scales horizontally-correct.
* B: Delete-Stops service, not a fix-incorrect.
* C: Fewer instances-Worsens issue-incorrect.
* D: Larger VM-Scales vertically-correct.
* E: Reduce bandwidth-Controls load-correct.
* Reasoning: A and D increase capacity, E manages demand-effective trio.
* Conclusion: A, D, E are correct.
OCI documentation states: "To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates." B stops service, C reduces capacity- only A, D, E align with OCI's deployment scaling options.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".

NEW QUESTION # 139
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?

A. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
B. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
C. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
D. Rewrite your code so that it is a single executable Python or Bash/Shell script file

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Run a complex Python project as an OCI Job.
* Evaluate Options:
* A: Auto-identification-False; entrypoint must be set.
* B: Rewrite-Unnecessary, inefficient.
* C: Auto-executable-False; needs explicit entrypoint.
* D: ZIP with entrypoint-Correct, flexible approach.
* Reasoning: D preserves structure, specifies execution.
* Conclusion: D is correct.
OCI documentation states: "For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py)." Auto-detection (A, C) isn't supported, and B discards structure-D is best.
Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

NEW QUESTION # 140
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