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2025 Latest Oracle 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional Test Questions Pdf

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q153-Q158):

NEW QUESTION # 153
Which is NOT a valid OCI Data Science notebook session approach?

A. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
B. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
C. Ensure you don't execute long-running Python processes in a notebook cell
D. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume
E. Authenticate using your notebook session's resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API approach

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an invalid notebook session practice.
* Evaluate Options:
* A: Valid-Long runs should use Jobs, not notebooks.
* B: Valid-Terminal runs with logging are supported.
* C: Valid-Multi-user conflicts are a concern.
* D: Invalid-Copying from Object Storage to block volume is unnecessary; direct access is best.
* E: Valid-Resource principals are secure and recommended.
* Reasoning: D contradicts OCI's direct-access efficiency.
* Conclusion: D is incorrect.
OCI documentation advises: "Access data in Object Storage directly from notebook sessions using SDKs or resource principals-avoid local copies (D) unless necessary." A, B, C, and E are best practices-D is inefficient and not standard.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".

NEW QUESTION # 154
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?

A. Data Flow
B. Anomaly Detection
C. Data Science
D. Data Labeling

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for scalable Spark applications.
* Evaluate Options:
* A: Data Science-ML platform, not Spark-focused.
* B: Anomaly Detection-Specific ML service, not general Spark.
* C: Data Labeling-Annotation tool, not Spark-related.
* D: Data Flow-Managed Spark service for big data.
* Reasoning: Data Flow is OCI's Spark execution engine.
* Conclusion: D is correct.
OCI Data Flow "provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks." Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution-B and C are unrelated.
Oracle Cloud Infrastructure Data Flow Documentation, "Overview".

NEW QUESTION # 155
Which activity is NOT a part of the machine learning life cycle?

A. Database Management
B. Modeling
C. Data Access
D. Model Deployment

Answer: A

Explanation:
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".

NEW QUESTION # 156
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 metadata tags
B. Check for provenance details
C. Check for model taxonomy details
D. Refer to the code inside the model

Answer: B

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 # 157
On which option do you set Oracle Cloud Infrastructure Budget?

A. Tenancy
B. Instances
C. Free-form tags
D. Compartments

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".

NEW QUESTION # 158
......

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Sean Cook Sean Cook

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