Oracle Cloud 1Z0-1110-26

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

Question 1

Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?

A. Jobs
B. Notebook Sessions
C. Model Catalog
D. Projects

Question 2

What is feature engineering in machine learning used for?

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

Question 3

You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing dat a. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?

A. You can install any open-source package available in a publicly accessible Python Package Index (PyPI) repository
B. OCI Data Science allows root privileges in notebook sessions
C. You can only install libraries using yum and pip as a normal user
D. You cannot install a library that’s not preinstalled in the provided image
E. You can install private or custom libraries from your own internal repositories

Question 4

You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?

A. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job
B. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
C. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
D. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads

Question 5

Which step is a part of the AutoML pipeline?

A. Model saved to Model Catalog
B. Feature Selection
C. Feature Extraction
D. Model Deployment

Solutions: