Study Guide Oracle 1z0-1110-25 Pdf | Exam 1z0-1110-25 Quiz

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Success in the 1z0-1110-25 certification exam is essential to advance your career. The Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) certification can set you apart from the competition and give you the edge you need to grow in your career. However, preparing for the 1z0-1110-25 test can be challenging, mainly if you have limited time. Here's where TorrentValid comes in with actual 1z0-1110-25 Questions. We at TorrentValid are well aware of the importance of the Oracle 1z0-1110-25 certification in order to stand out in today's competitive job environment.

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	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	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 5	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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q75-Q80):

NEW QUESTION # 75
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. Refer to the code inside the model
C. Check for model taxonomy details
D. Check for provenance details

Answer: D

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 # 76
Which statement is true about standards?

A. They are methods and instructions on how to maintain or accomplish the directives of the policy
B. They may be audited
C. They are the result of a regulation or contractual requirement or an industry requirement
D. They are the foundation of corporate governance

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about standards in an OCI context (likely governance/security).
* Understand Standards: Rules or benchmarks, often compliance-related.
* Evaluate Options:
* A: Auditable-True; standards are checked for adherence.
* B: Result of requirements-Partially true, but not always.
* C: Methods/instructions-More procedural, not defining standards.
* D: Foundation of governance-Broad, not specific to standards.
* Reasoning: A is universally true-standards face audits (e.g., SOC, ISO).
* Conclusion: A is correct.
OCI documentation notes: "Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations." B is a source, C describes procedures, D is too vague-only A is consistently true per OCI's compliance framework.
Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".

NEW QUESTION # 77
Where are OCI secrets stored?

A. OCI Vault
B. Autonomous Data Warehouse
C. OCI Object Storage
D. Oracle Databases

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.
* Evaluate Options:
* A: Object Storage is for general data, not secure secret management.
* B: Vault is OCI's service for storing and managing secrets securely.
* C: Autonomous Data Warehouse is for analytics, not secret storage.
* D: Oracle Databases store data, not OCI-specific secrets.
* Reasoning: Vault is purpose-built for secrets with encryption and access control.
* Conclusion: B is correct.
OCI Vault "provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies." Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes-only Vault (B) is designed for secrets per OCI's security architecture.
Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

NEW QUESTION # 78
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?

A. Adaptive Sampling
B. Algorithm Selection
C. Feature Selection
D. Model Deployment
E. Feature Extraction

Answer: A,B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three stages in OCI AutoML Pipeline.
* Understand Pipeline: Automates ML steps from data to model training.
* Evaluate Options:
* A: Feature Selection-Selects relevant features-correct.
* B: Adaptive Sampling-Reduces data size-correct.
* C: Model Deployment-Post-pipeline step-incorrect.
* D: Feature Extraction-Not explicit in OCI AutoML-incorrect.
* E: Algorithm Selection-Chooses best model-correct.
* Reasoning: A, B, E are core automated stages; C and D are separate.
* Conclusion: A, B, E are correct.
OCI documentation lists "AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning." Deployment (C) is post-pipeline, and extraction (D) isn't highlighted-only A, B, E are included per OCI's design.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

NEW QUESTION # 79
Which technique can be used for feature engineering in the machine learning lifecycle?

A. Support Vector Machines (SVM)
B. Principal Component Analysis (PCA)
C. Gradient boosting
D. K-means clustering

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

NEW QUESTION # 80
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Study Guide Oracle 1z0-1110-25 Pdf | Exam 1z0-1110-25 Quiz

Oracle 1z0-1110-25 Exam Syllabus Topics:

Pass Guaranteed Quiz 2025 Oracle 1z0-1110-25 – Trustable Study Guide Pdf

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q75-Q80):

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