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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	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 3	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 4	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 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 (Q116-Q121):

NEW QUESTION # 116
You have just completed analyzing a set of images by using Oracle Cloud Infrastructure (OCI) Data Labeling, and you want to export the annotated data. Which TWO formats are supported?

A. Data Labeling Service Proprietary JSON
B. CONLL V2003
C. Spacy
D. COCO

Answer: A,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify export formats for OCI Data Labeling.
* Understand Export: Annotated data is exported for ML use.
* Evaluate Options:
* A: CONLL V2003-Text-specific, not OCI-supported.
* B: COCO-Standard for image annotations-correct.
* C: Proprietary JSON-OCI's default format-correct.
* D: Spacy-NLP format, not supported.
* Reasoning: B and C are image-compatible per OCI docs.
* Conclusion: B and C are correct.
OCI documentation states: "Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service." CONLL (A) and Spacy (D) are text/NLP-focused-not supported for OCI's image annotations.
Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".

NEW QUESTION # 117
Which CLI command allows the customized conda environment to be shared with co-workers?

A. odsc conda install
B. odsc conda publish
C. odsc conda modify
D. odsc conda clone

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Share a custom conda environment in OCI Data Science.
* Understand Commands: OCI provides odsc CLI for environment management.
* Evaluate Options:
* A: clone duplicates an environment locally-not for sharing.
* B: publish uploads the environment to Object Storage for team access-correct.
* C: modify doesn't exist as a standard command.
* D: install sets up an environment locally-not for sharing.
* Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
* Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: "Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users." clone (A) is for local duplication, modify (C) isn't valid, and install (D) is for local setup-not sharing. B is the designated sharing mechanism.
Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".

NEW QUESTION # 118
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 model taxonomy details
C. Refer to the code inside the model
D. Check for metadata tags

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 # 119
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?

A. Retrain the model
B. Validate the model using recent data
C. Drift monitoring
D. Redeploy the model
E. Operational monitoring

Answer: A,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining model performance and improve churn prediction.
* Analyze Issue: Poor predictions suggest data drift or model staleness-common ML challenges.
* Evaluate Options:
* A. Drift monitoring: Tracks changes in data distribution-identifies root cause of accuracy drop- correct.
* B. Redeploy the model: Repeats deployment without fixing the issue-ineffective alone.
* C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.
* D. Retrain the model: Updates model with new data-directly improves accuracy-correct.
* E. Validate with recent data: Checks performance but doesn't fix-diagnostic, not corrective.
* Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)-best combo to verify and lower churn.
* Conclusion: A and D are correct.
OCI documentation advises: "Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy." Redeployment (B) doesn't address drift, operational monitoring (C) focuses on infra, and validation (E) is a check-not a fix. A and D align with OCI' s model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

NEW QUESTION # 120
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?

A. Jobs provisions the infrastructure to run a process on-demand
B. You must use a single Shell/Bash or Python artifact to run a job
C. You must create and manage your own Jobs infrastructure
D. Jobs comes with a set of standard tasks that cannot be customized

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Data Science Jobs.
* Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
* Evaluate Options:
* A: True-Jobs provision OCI compute resources on-demand for task execution.
* B: False-Users define custom tasks (e.g., Python scripts), not limited to standard ones.
* C: False-Infrastructure is fully managed by OCI, not user-managed.
* D: False-Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
* Reasoning: A reflects OCI's managed, on-demand provisioning model for Jobs.
* Conclusion: A is correct.
The OCI Data Science documentation states: "Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources." B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility-only A accurately describes Jobs.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".

NEW QUESTION # 121
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VCEPrep Oracle 1z0-1110-25 Different Formats

Oracle 1z0-1110-25 Exam Syllabus Topics:

Best Way To Ensure Success With Oracle 1z0-1110-25 Exam Questions

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q116-Q121):

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