1Z0-1110-25 VALID DUMPS PDF & 1Z0-1110-25 VALID EXAM OBJECTIVES

1z0-1110-25 Valid Dumps Pdf & 1z0-1110-25 Valid Exam Objectives

1z0-1110-25 Valid Dumps Pdf & 1z0-1110-25 Valid Exam Objectives

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q145-Q150):

NEW QUESTION # 145
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?

  • A. runtime.yaml
  • B. model_artifact_validate.py
  • C. requirements.txt
  • D. score.py

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Specify third-party libraries for model deployment.
* Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
* Evaluate Options:
* A: Not a standard file-incorrect.
* B: Inference code-not for dependencies.
* C: Defines conda env with dependencies-correct.
* D: Pip list-not used in OCI conda deployments.
* Reasoning: runtime.yaml points to a conda env with all libraries.
* Conclusion: C is correct.
OCI documentation states: "In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model." score.py (B) is for logic, requirements.txt (D) isn't OCI-standard, and A doesn't exist-C fixes the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".


NEW QUESTION # 146
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?

  • A. Create a group and add a policy
  • B. Create a dynamic group and add your instance
  • C. No configuration is required for making API calls
  • D. Create a dynamic group and add a policy

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Enable credential-less API calls from an instance.
* Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
* Evaluate Options:
* A: Dynamic group + policy-Correct; groups instance, grants access.
* B: Dynamic group only-Incomplete; needs policy.
* C: User group-Irrelevant for instances.
* D: No config-False; setup required.
* Reasoning: A sets up resource principal fully-group and perms.
* Conclusion: A is correct.
OCI documentation states: "To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services-enables resource principal." B lacks policy, C is user-based, D is false-only A completes the process per OCI's IAM setup.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".


NEW QUESTION # 147
What is the minimum active storage duration for logs used by Logging Analytics to be archived?

  • A. 30 days
  • B. 15 days
  • C. 60 days
  • D. 10 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 # 148
Which type of file system does File Storage use?

  • A. iSCSI
  • B. NFSv3
  • C. NVMe SSD
  • D. Paravirtualized

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the file system type for OCI File Storage.
* Understand File Storage: Network-attached storage in OCI.
* Evaluate Options:
* A: NFSv3-Network File System, used by File Storage-correct.
* B: iSCSI-Block storage protocol, not File Storage.
* C: Paravirtualized-Virtualization mode, not file system.
* D: NVMe SSD-Hardware, not file system.
* Reasoning: NFSv3 is OCI File Storage's protocol.
* Conclusion: A is correct.
OCI documentation states: "File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances." B, C, and D are unrelated-only A aligns with OCI's File Storage design.
Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".


NEW QUESTION # 149
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 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.
  • 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 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.
  • D. 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.

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 # 150
......

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