PASS GUARANTEED 2025 TRUSTABLE 1Z0-1110-25: ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL PRACTICE TEST ENGINE

Pass Guaranteed 2025 Trustable 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional Practice Test Engine

Pass Guaranteed 2025 Trustable 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional Practice Test Engine

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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 (Q12-Q17):

NEW QUESTION # 12
Which Oracle Cloud Service provides restricted access to target resources?

  • A. Bastion
  • B. SSL Certificate
  • C. Load Balancer
  • D. Internet Gateway

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for restricted resource access.
* Evaluate Options:
* A: Bastion-Secure, temporary access to resources-correct.
* B: Internet Gateway-Public access, not restricted.
* C: Load Balancer-Distributes traffic, not access control.
* D: SSL Certificate-Secures comms, not access.
* Reasoning: Bastion limits access (e.g., SSH) to specific targets.
* Conclusion: A is correct.
OCI documentation states: "OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH." B, C, and D don't restrict-only A fits per OCI's security services.
Oracle Cloud Infrastructure Bastion Documentation, "Overview".


NEW QUESTION # 13
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?

  • A. Oracle Cloud Infrastructure Data Science
  • B. Oracle Cloud Infrastructure Data Integration
  • C. Oracle Cloud Infrastructure Data Flow
  • D. Oracle Cloud Infrastructure Data Catalog

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for ML model lifecycle.
* Evaluate Options:
* A: Data Catalog-Metadata management, not ML.
* B: Data Integration-ETL, not ML.
* C: Data Science-Full ML lifecycle-correct.
* D: Data Flow-Spark processing, not full ML.
* Reasoning: C supports building, training, deploying models.
* Conclusion: C is correct.
OCI documentation states: "OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog." A, B, and D serve other purposes-only C fits the ML lifecycle per OCI's offerings.
Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".


NEW QUESTION # 14
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

  • A. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimization and performance tests in advance to identify the right compute shape before running the model training as a job.
  • B. Start with a small shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve.
  • C. Start with a smaller shape and monitor the job run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized.
  • D. Start with the strongest compute shape Jobs support and monitor the job run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find optimal compute shape balancing cost and time.
* Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
* Evaluate Options:
* A: Tuning parameters when underutilized-focuses on model, not shape optimization.
* B: Strongest shape-Costly, ignores balance; overkill likely.
* C: Scale up from small shape when fully utilized-Balances cost/time effectively.
* D: Random start with pre-tests-Unsystematic and inefficient.
* Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
* Conclusion: C is correct.
OCI documentation advises: "To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus-balancing cost and speed." A misfocuses on model tuning, B wastes cost, and D lacks structure- only C aligns with this method.
Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".


NEW QUESTION # 15
You are a researcher who requires access to large datasets. Which OCI service would you use?

  • A. Oracle Open Data
  • B. OCI Data Science
  • C. ADW (Autonomous Data Warehouse)
  • D. Oracle Databases

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for accessing large public datasets.
* Evaluate Options:
* A: Oracle Databases-General-purpose, not dataset-focused.
* B: ADW-Analytics warehouse, not a dataset repository.
* C: OCI Data Science-ML platform, not a dataset provider.
* D: Oracle Open Data-Free, public datasets (e.g., geospatial).
* Reasoning: Open Data provides pre-existing large datasets for research.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers." Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML-not datasets-only Open Data (D) fits.
Oracle Cloud Infrastructure Open Data Documentation, "Overview".


NEW QUESTION # 16
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. Manage
  • B. Read
  • 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 # 17
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