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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Objectives
OCI Data Science - Introduction and Configuration	- Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions
Design and Set Up Data Science Workspace	- Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects
Use Related OCI Services	- Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing
Implement End-to-End Machine Learning Lifecycle	- Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models - Prepare and manage datasets - Deploy models and consume model endpoints - Save and manage models using Model Catalog
Apply MLOps Practices	- Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q32-Q37):

NEW QUESTION # 32
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?

A. Jobs comes with a set of standard tasks that cannot be customized
B. Jobs provisions the infrastructure to run a process on-demand
C. You must use a single Shell/Bash or Python artifact to run a job
D. You must create and manage your own Jobs infrastructure

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Data Science Jobs.
Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
Evaluate Options:
A: True—Jobs provision OCI compute resources on-demand for task execution.
B: False—Users define custom tasks (e.g., Python scripts), not limited to standard ones.
C: False—Infrastructure is fully managed by OCI, not user-managed.
D: False—Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
Reasoning: A reflects OCI’s managed, on-demand provisioning model for Jobs.
Conclusion: A is correct.
The OCI Data Science documentation states: “Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility—only A accurately describes Jobs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".

NEW QUESTION # 33
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?

A. DataFrameLabelEncode()
B. auto_transform()
C. OneHotEncoder()
D. show_in_notebook()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Encode categorical features in a Data Science context (likely ADS SDK).
Understand Encoding: Converts categories (e.g., countries) to numerical forms.
Evaluate Options:
A: Not a standard ADS method—incorrect.
B: General transformation, not specific encoding—incorrect.
C: OneHotEncoder—Standard for categorical encoding—correct.
D: Visualization, not encoding—incorrect.
Reasoning: One-hot encoding creates binary columns—ideal for multiple categories.
Conclusion: C is correct.
OCI documentation states: “In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like ‘countries’ into binary vectors for modeling.” A isn’t real, B is too broad, D is unrelated—only C fits OCI’s encoding practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".

NEW QUESTION # 34
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?

A. fit_transform()
B. visualize_transforms()
C. get_transformed_dataset()
D. auto_transform()

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Apply all recommended transformations from get_recommendations() in ADS.
Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
Evaluate Options:
A: Returns transformed data—Not for applying—incorrect.
B: Sklearn-style, not ADS-specific—incorrect.
C: auto_transform()—Applies all recommendations—correct.
D: Visualizes, doesn’t apply—incorrect.
Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
Conclusion: C is correct.
OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".

NEW QUESTION # 35
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create your code to expect different parameters as command-line arguments, and create a new job every time you run the code
B. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
C. Create a new job every time you need to run your code and pass the parameters as environment variables
D. Create a new job by setting the required parameters in your code, and create a new job for every code change

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
Evaluate Options:
A: New job per run with env vars—Redundant job creation, inefficient.
B: New job per run with args—Similarly inefficient due to repeated setup.
C: Hardcode params, new job per change—Highly inefficient, requires code edits.
D: Single job, flexible params via env vars or args—Efficient, reusable—correct.
Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.
Conclusion: D is correct.
OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

NEW QUESTION # 36
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?

A. Each job run sends outputs to a single log for that job
B. Logs are automatically deleted when the job and job run is deleted
C. All stdout and stderr are automatically stored when automatic log creation is enabled
D. Integrating data science jobs resources with logging is mandatory

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Jobs logging.
Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
Evaluate Options:
A: False—Each run has its own log, not a single job log.
B: False—Logging is optional, not mandatory.
C: True—When enabled, stdout/stderr are auto-captured.
D: False—Logs persist unless explicitly deleted.
Reasoning: C matches OCI’s automatic logging feature.
Conclusion: C is correct.
OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".

NEW QUESTION # 37
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