Three Formats OF 1z0-1110-25 Practice Material By DumpStillValid

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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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Reliable 1z0-1110-25 Practice Exam Learning Materials: Oracle Cloud Infrastructure 2025 Data Science Professional - DumpStillValid

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q128-Q133):

NEW QUESTION # 128
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?

A. Global and local behaviours of machine learning models are similar
B. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model
C. Local explanation techniques are model-agnostic, while global explanation techniques are not
D. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define LIME's core concept.
* Understand LIME: Explains individual predictions with local surrogate models.
* Evaluate Options:
* A: Complex global, simple local-Correct LIME principle.
* B: Agnosticism-True but not the key idea.
* C: Global/local similarity-False.
* D: Local vs. global agnosticism-Incorrect distinction.
* Reasoning: A captures LIME's local approximation focus.
* Conclusion: A is correct.
OCI documentation notes: "LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances." B, C, and D misalign-only A reflects LIME's foundational idea per OCI's interpretability tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".

NEW QUESTION # 129
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?

A. No configuration is required for making API calls
B. Create a dynamic group and add your instance
C. Create a group and add a policy
D. Create a dynamic group and add a policy

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Enable credential-less API calls from an instance.
* Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
* Evaluate Options:
* A: Dynamic group + policy-Correct; groups instance, grants access.
* B: Dynamic group only-Incomplete; needs policy.
* C: User group-Irrelevant for instances.
* D: No config-False; setup required.
* Reasoning: A sets up resource principal fully-group and perms.
* Conclusion: A is correct.
OCI documentation states: "To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services-enables resource principal." B lacks policy, C is user-based, D is false-only A completes the process per OCI's IAM setup.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".

NEW QUESTION # 130
How can you collaborate with team members in OCI Data Science Workspace?

A. By granting access to specific notebooks and files
B. By using version control systems integrated with the workspace
C. By sharing the workspace instance with other users
D. By enabling chat and video conferencing within the workspace

Answer: B

Explanation:
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".

NEW QUESTION # 131
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
* Model 1 has Test accuracy is 80% and recall is 70%
* Model 2 has Test accuracy is 75% and recall is 85%

A. Model 1, because recall has lesser impact on predictions in this use case
B. Model 2, because recall has more impact on predictions in this use case
C. Model 1, because the test accuracy is high
D. Model 2, because recall is high

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose the better model for cancer prediction based on metrics.
* Understand Metrics:
* Accuracy: Overall correct predictions.
* Recall: True positives / (True positives + False negatives)-crucial for cancer (minimizing misses).
* Context: Cancer prediction prioritizes recall-false negatives (missed cancers) are critical.
* Evaluate Models:
* Model 1: 80% accuracy, 70% recall-Misses more cancers.
* Model 2: 75% accuracy, 85% recall-Misses fewer cancers.
* Evaluate Options:
* A: High recall-True, but lacks context.
* B: High accuracy-Misses recall's importance.
* C: Recall's impact-Correct for cancer use case-best.
* D: Lesser recall impact-Incorrect for this priority.
* Reasoning: C emphasizes recall's critical role-aligns with medical needs.
* Conclusion: C is correct.
OCI documentation advises: "For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at
85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C)." A is partial, B overlooks context, D reverses priority-only C fits OCI's ML evaluation guidance for this scenario.
Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

NEW QUESTION # 132
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?

A. Oracle Open Data
B. Oracle Cloud Infrastructure (OCI) Data Science
C. Oracle Analytics Cloud
D. Oracle Big Data Service

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for free geospatial images.
* Evaluate Options:
* A: Big Data Service-Spark processing, not datasets.
* B: Analytics Cloud-Visualization, not data source.
* C: Data Science-ML platform, not dataset provider.
* D: Open Data-Free public datasets, including geospatial-correct.
* Reasoning: Open Data is OCI's public dataset hub.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to curated datasets, including geospatial images, for public use." A, B, and C serve other purposes-only D delivers free geospatial data.
Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".

NEW QUESTION # 133
......

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THREE FORMATS OF 1Z0-1110-25 PRACTICE MATERIAL BY DUMPSTILLVALID

Three Formats OF 1z0-1110-25 Practice Material By DumpStillValid