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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

Question 1

Which is NOT a valid OCI Data Science notebook session approach?

A. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
B. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume
C. Authenticate using your notebook session’s resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API approach
D. Ensure you don’t execute long-running Python processes in a notebook cell
E. Run the process directly in the terminal and use Python logging to get updates on the progress of your job

Question 2

You have just completed analyzing a set of images by using Oracle Cloud Infrastructure (OCI) Data Labeling, and you want to export the annotated dat a. Which TWO formats are supported?

A. Data Labeling Service Proprietary JSON
B. Spacy
C. COCO
D. CONLL V2003

Question 3

You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?

A. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
B. Global and local behaviours of machine learning models are similar
C. Local explanation techniques are model-agnostic, while global explanation techniques are not
D. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model

Question 4

Which step is unique to MLOps, as opposed to DevOps?

A. Continuous delivery
B. Continuous training
C. Continuous integration
D. Continuous deployment

Question 5

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. The current best-known hyperparameter configuration
C. A random hyperparameter configuration
D. A hyperparameter configuration with a minimum learning rate

Solutions: