1Z0-1110-25 RELIABLE EXAM QUESTIONS | 1Z0-1110-25 BRAINDUMP PDF

1z0-1110-25 Reliable Exam Questions | 1z0-1110-25 Braindump Pdf

1z0-1110-25 Reliable Exam Questions | 1z0-1110-25 Braindump Pdf

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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
  • 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 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
  • 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q113-Q118):

NEW QUESTION # 113
Where do calls to stdout and stderr from score.py go in the model deployment?

  • A. The OCI Cloud Shell, which can be accessed from the console
  • B. The OCI Console
  • C. The file that was defined for them on the virtual machine (VM)
  • D. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate stdout/stderr output from score.py in deployment.
* Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
* Evaluate Options:
* A: False-No VM file is defined by default; logs go to OCI Logging.
* B: False-Console displays UI, not raw logs.
* C: False-Cloud Shell is a separate tool, not a log destination.
* D: True-Predict logs in OCI Logging capture stdout/stderr.
* Reasoning: OCI centralizes logs in its Logging service for deployments.
* Conclusion: D is correct.
OCI documentation states: "During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings." A, B, and C don't align with this managed logging approach-only D is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".


NEW QUESTION # 114
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?

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

Answer: C

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 # 115
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?

  • A. It is an important tool for detecting fraud, network intrusions, and discrepancies in sensor time series analysis
  • B. It is trained on a combination of customer and general industry datasets
  • C. Accepted file types are SQL and Python
  • D. Data used for analysis can be text or numerical in nature

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about OCI Anomaly Detection.
* Understand Service: Detects anomalies in multivariate data (e.g., time series).
* Evaluate Options:
* A: False-Accepted types are CSV/JSON, not SQL/Python.
* B: Partially true-Focuses on numerical data (e.g., sensors), not text broadly.
* C: True-Used for fraud, intrusions, and sensor anomalies (key use cases).
* D: False-Trained on customer data only, not general datasets.
* Reasoning: C aligns with documented applications; others misalign.
* Conclusion: C is correct.
OCI Anomaly Detection documentation states: "The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies." A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data-only C matches the service's purpose.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".


NEW QUESTION # 116
Which type of file system does File Storage use?

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

Answer: D

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 # 117
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?

  • A. Local Interpretable Model-Agnostic Explanations
  • B. Feature Dependence Explanations
  • C. Accumulated Local Effects
  • D. Feature Permutation Importance Explanations

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".


NEW QUESTION # 118
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