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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

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

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

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 (Q92-Q97):

NEW QUESTION # 92
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?

A. Making a commit that is taking a snapshot of the local repository for the next push
B. Converting an existing local project folder to a Git repository
C. Updating the local repo to match the content from a remote repository
D. Moving the changes into staging area for the next commit
E. Pushing changes to a remote repository

Answer: C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze Git Operations: Identify which depend on internet speed.
* Evaluate Options:
* A. Staging (git add): Local operation-adds files to the index; no network involved.
* B. Updating local repo (git pull): Downloads remote changes-requires internet, slowed by poor connectivity.
* C. Pushing changes (git push): Uploads local commits to remote-network-dependent, delayed by slow speed.
* D. Committing (git commit): Local snapshot-no network needed.
* E. Converting to Git repo (git init): Local initialization-no internet required.
* Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
* Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as "bandwidth-sensitive" in OCI's guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user's machine, unaffected by internet speed. This matches standard Git behavior and OCI's implementation.
Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

NEW QUESTION # 93
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?

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

Answer: A

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 # 94
You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (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 networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway
C. The policy for your user group grants manage permissions for the model catalog in this compartment
D. A dynamic group has rules that match the notebook sessions in its compartment
E. The policy for a dynamic group grants manage permissions for the model catalog in this compartment

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 auth error when saving a model with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials.
* Analyze 404 Error: Indicates permission failure-likely IAM misconfiguration.
* Evaluate Options:
* A: Block volume storage-Irrelevant to auth; it's about saving locally-incorrect.
* B: Dynamic group matching-Ensures notebook is recognized-correct.
* C: User group policy-Not used with resource principal-incorrect.
* D: Dynamic group policy-Grants catalog access-correct.
* E: Service Gateway-Network-related, not auth-specific-incorrect.
* Reasoning: Resource principal needs B (group inclusion) and D (policy perms)-404 points to these.
* Conclusion: B and D are correct.
OCI documentation states: "For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type
='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D)." A is storage, C is user-based, E is network-only B and D fix the auth issue per OCI's IAM setup.
Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".

NEW QUESTION # 95
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?

A. Steps in a pipeline can be run only sequentially
B. Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)
C. Parallel steps cannot be run if they are completely independent of each other
D. All pipeline steps are always run in parallel

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand parallel execution in OCI Pipelines.
* Evaluate Options:
* A: False-Pipelines support parallelism.
* B: True-DAG allows sequential/parallel steps-correct.
* C: False-Not all steps must be parallel.
* D: False-Independence enables parallelism.
* Reasoning: B reflects OCI's DAG-based flexibility.
* Conclusion: B is correct.
OCI documentation states: "Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time." A, C, and D misrepresent this-only B aligns with OCI's pipeline design.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".

NEW QUESTION # 96
You want to make your model more parsimonious to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

A. pearson_plot()
B. cramersv_plot()
C. correlation_ratio_plot()
D. corr()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features using ADS SDK.
* Understand Correlation Types:
* Continuous vs. Continuous: Pearson correlation.
* Categorical vs. Categorical: Cramer's V.
* Continuous vs. Categorical: Correlation ratio (eta).
* Evaluate Options:
* A. corr(): General correlation (Pearson), not suited for mixed types-incorrect.
* B. correlation_ratio_plot(): Plots correlation ratio for continuous-categorical-correct.
* C. pearson_plot(): Not an ADS method; Pearson is continuous-only-incorrect.
* D. cramersv_plot(): Cramer's V for categorical-categorical-incorrect.
* Reasoning: Correlation ratio measures association between continuous and categorical variables-ideal for heatmap in this mixed scenario.
* Conclusion: B is correct.
OCI documentation states: "The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal." corr() (A) defaults to Pearson, pearson_plot() (C) isn't real, and cramersv_plot() (D) is for categorical pairs-only B aligns with OCI's ADS capabilities for this use case.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".

NEW QUESTION # 97
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