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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Topic 3: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Topic 4: Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Topic 5: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Prepare and manage datasets - Build, train, and evaluate machine learning models |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
Question 1
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?
A. score.py
B. requirements.txt
C. runtime.yaml
D. model_artifact_validate.py
Question 2
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
A. Detailed
B. Perfunctory
C. Pass a dictionary that defines a search space
D. ADSTuner doesn’t need a search space to tune the hyperparameters
Question 3
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?
A. The last generated hyperparameter configuration
B. A hyperparameter configuration with a minimum learning rate
C. A random hyperparameter configuration
D. The current best-known hyperparameter configuration
Question 4
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?
A. Explained variance score
B. Recall
C. F1-score
D. R-squared
E. Mean squared error
Question 5
The feature type TechJob has the following registered validators:
TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_default_handler) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_open_handler, condition=('job_family',)) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_closed_handler, condition=('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?
A. Execute the is_tech_job_default_handler handler
B. Throw an error because the system cannot determine which handler to run
C. Execute the is_tech_job_open_handler handler
D. Execute the is_tech_job_closed_handler handler
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: B,C | Question 5 Answer: C |
