VALUABLE 1Z0-1110-25 FEEDBACK & STUDY 1Z0-1110-25 CENTER

Valuable 1z0-1110-25 Feedback & Study 1z0-1110-25 Center

Valuable 1z0-1110-25 Feedback & Study 1z0-1110-25 Center

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Exam4Free Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) practice test material covers all the key topics and areas of knowledge necessary to master the Oracle Certification Exam. Experienced industry professionals design the 1z0-1110-25 exam questions and are regularly updated to reflect the latest changes in the Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) exam. In addition, Exam4Free offers three different formats of practice material which are discussed below.

Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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
  • 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 (Q83-Q88):

NEW QUESTION # 83
What is feature engineering in machine learning used for?

  • A. To transform existing features into new ones
  • B. To help understand the dataset features
  • C. To interpret ML models
  • D. To perform parameter tuning

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Feature Engineering: It's the process of creating or modifying features to improve model performance.
* Evaluate Options:
* A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
* B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
* C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering-correct.
* D: Understanding features occurs during exploration, not engineering.
* Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of- week), distinct from tuning or interpretation.
* Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as "the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy." Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".


NEW QUESTION # 84
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?

  • A. The model artifact is saved to the block volume of the notebook session
  • B. The policy for your user group grants manage permissions for the model catalog in this compartment
  • C. The dynamic groups matching rule exists for notebook sessions in the compartment
  • D. The networking configuration allows access to the Oracle Cloud Infrastructure services through a service gateway
  • E. The policy for the dynamic group grants manage permissions for the model catalog in this compartment

Answer: C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials needed.
* Analyze 404 Error: Indicates an authorization failure-likely missing permissions or misconfigured resource principal.
* Evaluate Options:
* A: True-Dynamic group must include notebook sessions (e.g., resource.type =
'datasciencenotebooksession') to authenticate.
* B: False-Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
* C: True-Policy must grant manage data-science-models to the dynamic group for catalog access.
* D: False-Service gateway ensures network access, but 404 is auth-related, not connectivity.
* E: False-Resource principal uses dynamic group policies, not user group policies.
* Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth- others are tangential.
* Conclusion: A and C are correct.
OCI documentation states: "To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type =
'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment." B is unrelated (storage location), D is network-focused, and E applies to user auth-not resource principal. A 404 error flags missing auth, fixed by A and C.
Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".


NEW QUESTION # 85
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?

  • A. OCI Speech
  • B. OCI Vision
  • C. OCI Language
  • D. Oracle Digital Assistant

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service to sort images by language.
* Evaluate Options:
* A: Digital Assistant-Chatbots, not image/language processing.
* B: Vision-Image analysis (e.g., object detection), not language sorting.
* C: Speech-Audio-to-text, not image-based.
* D: Language-Text analysis (e.g., language detection) after OCR-correct.
* Reasoning: Images need OCR (Vision) then language detection (Language)-D fits the sorting task.
* Conclusion: D is correct.
OCI Language "detects and classifies languages in text," often paired with OCI Vision's OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language-Digital Assistant (A) and Speech (C) don't apply. Documentation supports this workflow.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".


NEW QUESTION # 86
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?

  • A. The compartment of the Data Flow application
  • B. The path to the archive.zip file
  • C. The bucket used to read/write the PySpark script in Object Storage
  • D. The local path to your PySpark script
  • E. The display name of the application

Answer: A,C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three required params for an OCI Data Flow app config.
* Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
* Evaluate Options:
* A: Archive path-Optional if script is in Object Storage-incorrect.
* B: Local script path-Not needed; script is uploaded-incorrect.
* C: Compartment-Required for resource scope-correct.
* D: Bucket-Required for script storage/access-correct.
* E: Display name-Required for app identification-correct.
* Reasoning: C, D, E are mandatory metadata for Data Flow creation-script location is specified via bucket.
* Conclusion: C, D, E are correct.
OCI documentation states: "To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object." Local paths (B) or archives (A) are optional or handled separately-only C, D, E are required per OCI's Data Flow API spec.
Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".


NEW QUESTION # 87
Which of the following analytical and statistical techniques do data scientists commonly use?

  • A. Classification
  • B. All of the above
  • C. Regression
  • D. Clustering

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify common data science techniques.
* Define Techniques:
* Classification: Predicts categories (e.g., spam vs. not).
* Regression: Predicts continuous values (e.g., sales).
* Clustering: Groups data (e.g., customer segments).
* Evaluate Options:
* A, B, C: All are standard ML/statistical methods.
* D: Encompasses all-correct as they're widely used.
* Reasoning: These are foundational in data science workflows.
* Conclusion: D is correct.
OCI documentation lists "classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML." All (D) are common per OCI's ML framework, not just subsets (A, B, C).
Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".


NEW QUESTION # 88
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