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1z0-1110-25 Valid Mock Test - High Pass-Rate Oracle 1z0-1110-25 Questions Exam: Oracle Cloud Infrastructure 2025 Data Science Professional

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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

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 3

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 4

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 5

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q71-Q76):

NEW QUESTION # 71
True or false? Bias is a common problem in data science applications.

A. False
B. True

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess if bias is a common issue in data science.
* Define Bias: Systematic errors in data/models (e.g., skewed training data).
* Evaluate Statement:
* Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws-widely recognized in ML.
* Examples: Gender bias in hiring models, racial bias in facial recognition.
* Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
* Conclusion: A (True) is correct.
OCI documentation notes: "Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks." This aligns with industry standards-bias is a well-documented issue, making A true.
Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

NEW QUESTION # 72
Which architecture is based on the principle of "never trust, always verify"?

A. Zero trust
B. Defense in depth
C. Fluid perimeter
D. Federated identity

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the architecture with "never trust, always verify."
* Evaluate Options:
* A: Federated identity-Shares auth, not verification-focused.
* B: Zero trust-Explicitly "never trust, always verify"-correct.
* C: Fluid perimeter-Adaptive, not the core principle.
* D: Defense in depth-Layered, not verification-centric.
* Reasoning: Zero trust matches the stated principle exactly.
* Conclusion: B is correct.
OCI documentation states: "Zero trust (B) architecture operates on 'never trust, always verify,' requiring continuous authentication and authorization." A, C, and D have different focuses-only B aligns with OCI's security philosophy.
Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".

NEW QUESTION # 73
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?

A. R-Squared
B. Mean squared error
C. Recall
D. F1 Score
E. Explained variance score

Answer: C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".

NEW QUESTION # 74
You're going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate data. Where do you need to store the training data?

A. MySQL database
B. Object Storage Bucket
C. Autonomous Data Warehouse
D. Your local machine

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI's ecosystem.
* Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.
* Evaluate Options:
* A. Your local machine: Data on a local machine isn't directly accessible to OCI services without upload, making it impractical for cloud-based training.
* B. MySQL database: While OCI supports MySQL, Anomaly Detection doesn't natively integrate with it for training data; it prefers file-based input.
* C. Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.
* D. Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).
* Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.
* Conclusion: D is the correct choice as it aligns with the service's architecture.
The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must "upload the training dataset to an OCI Object Storage bucket" before creating a data asset for model training. Local storage (A) isn't viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren't supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.

NEW QUESTION # 75
Which of these is a unique feature of the published conda environment?

A. Provides availability on network session reactivation
B. Allows you to save the conda environment in a block volume
C. Provides a comprehensive environment to solve business use cases
D. Allows you to save the conda environment to an Object Storage Bucket

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.
* Evaluate Options:
* A: Vague-All conda environments can address use cases; not unique to "published."
* B: Incorrect-Availability on reactivation applies to session persistence, not publishing.
* C: Correct-Publishing saves the environment to Object Storage for sharing/reuse.
* D: Incorrect-Block volumes store session data, not published environments.
* Reasoning: The unique aspect of "published" environments is their storage in Object Storage (via odsc conda publish), enabling team access.
* Conclusion: C is the distinctive feature.
The OCI Data Science documentation highlights that "published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users." This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle's design.
Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.

NEW QUESTION # 76
......

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