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Oracle 1z0-1110-25 Simulation Questions | Free 1z0-1110-25 Brain Dumps

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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

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

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

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 (Q111-Q116):

NEW QUESTION # 111
What is the minimum active storage duration for logs used by Logging Analytics to be archived?

A. 30 days
B. 15 days
C. 10 days
D. 60 days

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine minimum log storage duration before archiving in Logging Analytics.
* Understand Logging Analytics: Logs are active before archival.
* Evaluate Options:
* A: 60 days-Too long for minimum.
* B: 10 days-Too short.
* C: 30 days-Standard minimum-correct.
* D: 15 days-Not OCI's default.
* Reasoning: 30 days is OCI's documented minimum active period.
* Conclusion: C is correct.
OCI documentation states: "Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis." B and D are shorter, A is longer-only C matches OCI's policy.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

NEW QUESTION # 112
You want to create a user group for a team of external data science consultants. The consultants should only have the ability to see Data Science resource details but not have the ability to create, delete, or update Data Science resources. What verb should you write in the policy?

A. Read
B. Manage
C. Use
D. Inspect

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI IAM Policies: Policies control access using verbs like "inspect," "read," "use," and
"manage."
* Define Requirement: Consultants need view-only access without modification rights.
* Evaluate Verbs:
* A. Use: Allows interaction (e.g., running jobs)-too permissive.
* B. Inspect: Grants view-only access to resource details-matches requirement.
* C. Manage: Full control (create, update, delete)-too permissive.
* D. Read: Includes viewing content (e.g., data), not just metadata-slightly broader than needed.
* Conclusion: "Inspect" (B) is the precise verb for view-only access to resource details.
In OCI Identity and Access Management (IAM), the "inspect" verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Reference: Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").

NEW QUESTION # 113
What is the correct definition of Git?

A. Git is a centralized version control system that allows you to revert to previous versions of files as needed.
B. Git is a centralized version control system that allows data scientists and developers to track copious amounts of data.
C. Git is a distributed version control system that protects teams from simultaneous repo contributions and merge requests.
D. Git is a distributed version control system that allows you to track changes made to a set of files.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Git: Git is a version control system-centralized vs. distributed is key.
* Evaluate Options:
* A: Incorrect-Git is distributed, not centralized (e.g., SVN is centralized).
* B: Correct-Distributed, tracks file changes across local and remote repos.
* C: Incorrect-Git allows simultaneous contributions; it manages, not prevents, merges.
* D: Incorrect-Centralized is wrong, and "copious data" is vague.
* Reasoning: Git's distributed nature (each user has a full repo copy) and change-tracking are core traits.
* Conclusion: B is accurate.
OCI documentation aligns with Git's official definition: "Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management." A and D misclassify it as centralized, while C misrepresents merge handling-B captures Git's essence as used in OCI Data Science.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".

NEW QUESTION # 114
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.

A. False
B. True

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".

NEW QUESTION # 115
What is the first step in the data science process?

A. Collecting data and preparing it for analysis
B. Defining an analytical hypothesis that could provide business value
C. Experimenting with and tuning different analytical models
D. Working with data owners

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the initial data science step.
* Define Process: Starts with problem definition, then data and modeling.
* Evaluate Options:
* A: Data collection-Second step after problem definition.
* B: Modeling-Later stage.
* C: Hypothesis-Sets the goal, first step-correct.
* D: Data owners-Collaboration, not the start.
* Reasoning: Hypothesis drives the process (e.g., "Can we predict churn?").
* Conclusion: C is correct.
OCI documentation states: "The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis." C precedes A, B, and D-aligning with OCI's structured approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".

NEW QUESTION # 116
......

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