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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 5	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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q123-Q128):

NEW QUESTION # 123
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?

A. Package your personal OCI config file and keys in the job artifact
B. Create a pre-authenticated request (PAR) for the Object Storage bucket and use that in the job code
C. Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal
D. Use the resource principal of the job run as the signer in the job code, ensuring there is a dynamic group for this job run with appropriate access to Object Storage and the model catalog

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Securely authenticate a recurring OCI Job.
* Evaluate Options:
* A: PAR-Limited duration, insecure for recurring jobs.
* B: Resource principal-Secure, managed auth for Jobs-correct.
* C: Personal config-Unscalable, security risk.
* D: Vault with personal keys-Complex, still uses user creds.
* Reasoning: B uses OCI's native, secure resource principal mechanism.
* Conclusion: B is correct.
OCI documentation states: "For Jobs accessing sensitive data, use resource principals with a dynamic group (e.
g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog- secure and scalable." A is temporary, C and D risk credential exposure-B is best practice.
Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

NEW QUESTION # 124
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

A. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
B. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
C. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
D. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

NEW QUESTION # 125
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. Parallel steps cannot be run if they are completely independent of each other
B. All pipeline steps are always run in parallel
C. Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)
D. Steps in a pipeline can be run only sequentially

Answer: C

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 # 126
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?

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

Answer: C

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 # 127
What is a common maxim about data scientists?

A. They spend 80% of their time finding and preparing data and 20% analyzing it.
B. They spend 80% of their time analyzing data and 20% finding and preparing it.
C. They spend 80% of their time on failed analytics projects and 20% doing useful work.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a widely accepted maxim about data scientists' time allocation.
* Understand Data Science Workflow: Involves data collection, preparation, and analysis-time distribution is key.
* Evaluate Options:
* A: 80% on finding/preparing, 20% analyzing-Reflects the data wrangling challenge.
* B: 80% analyzing, 20% finding/preparing-Inverts the common perception.
* C: 80% on failed projects, 20% useful-Pessimistic, not a standard maxim.
* Reasoning: Industry consensus (e.g., "80/20 rule") emphasizes data prep as the bulk of effort due to messy real-world data.
* Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: "Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data." B reverses this, and C isn't supported-only A reflects this widely cited maxim from sources like Forbes and OCI's practical guidance.
Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".

NEW QUESTION # 128
......

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VCE 1z0-1110-25 Exam Simulator - New 1z0-1110-25 Mock Exam

Oracle Cloud Infrastructure 2025 Data Science Professional Exam Questions Pdf & 1z0-1110-25 Test Training Demo & Oracle Cloud Infrastructure 2025 Data Science Professional Test Online Engine

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q123-Q128):

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