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Pass Guaranteed Quiz 2025 Oracle 1z0-1110-25: Trustable Reliable Oracle Cloud Infrastructure 2025 Data Science Professional Test Price

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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

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 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

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 (Q146-Q151):

NEW QUESTION # 146
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?

A. Use environmental variables
B. Use command-line variables
C. Configure a block volume
D. Configure a compute shape

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Configure conda env for a pipeline step.
* Evaluate Options:
* A: Shape-Infra, not env config.
* B: Volume-Storage, not env.
* C: Command-line-Step args, not env.
* D: Env variables-Sets conda path-correct.
* Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
* Conclusion: D is correct.
OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

NEW QUESTION # 147
What do you use the score.py file for?

A. Define the required conda environment
B. Execute the inference logic code
C. Define the scaling strategy
D. Configure the deployment infrastructure

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the purpose of score.py in OCI Data Science model deployment.
* Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.
yaml, etc.
* Evaluate Options:
* A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.
py.
* B: score.py contains the inference logic (e.g., load_model(), predict())-correct.
* C: Conda environment is defined in runtime.yaml or a requirements file-not score.py.
* D: Scaling (e.g., instance count) is set in deployment configuration-not score.py.
* Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
* Conclusion: B is the correct purpose.
The OCI Data Science documentation states: "The score.py file is a required artifact for model deployment, containing the inference logic-functions like load_model() to load the model and predict() to generate predictions from input data." Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI's deployment workflow.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".

NEW QUESTION # 148
Which of these is a unique feature of the published conda environment?

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

Answer: C

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 # 149
Which is NOT a part of Observability and Management Services?

A. Logging Analytics
B. OCI Management Service
C. Logging
D. Event Services

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-Observability and Management (O&M) service in OCI.
* Understand O&M: Includes monitoring, logging, events tools.
* Evaluate Options:
* A: Event Services-Triggers actions, part of O&M-correct.
* B: OCI Management Service-Not a defined O&M service-incorrect.
* C: Logging Analytics-Log analysis, O&M component-correct.
* D: Logging-Log collection, O&M component-correct.
* Reasoning: B isn't listed in OCI's O&M suite-others are.
* Conclusion: B is correct (not part of O&M).
OCI documentation lists "Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)-'OCI Management Service' (B) is not a recognized component." B appears to be a misnomer-only A, C, D are O&M per OCI's service catalog.
Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".

NEW QUESTION # 150
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?

A. Enable client-side encryption
B. Enable server-side encryption
C. Enable Versioning
D. Limit delete permissions
E. Setup volumes in a RAID1 configuration

Answer: C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two methods to enhance Object Storage durability.
* Understand Durability: Ensures data isn't lost-focus on redundancy and protection.
* Evaluate Options:
* A: RAID1-Block volume feature, not Object Storage.
* B: Encryption-Secures data, not durability.
* C: Versioning-Retains old versions, prevents loss-correct.
* D: Limit delete-Prevents accidental deletion-correct.
* E: Client encryption-Secures, not durability-focused.
* Reasoning: C and D directly protect against data loss-durability-focused.
* Conclusion: C and D are correct.
OCI documentation states: "Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss." A isn't applicable, B and E focus on security-only C and D enhance durability per OCI's storage features.
Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".

NEW QUESTION # 151
......

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