Oracle Cloud Infrastructure Data Science Professional - 1Z0-1110-26

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

Question 1

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. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads
B. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
C. 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
D. 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

Question 2

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

Question 3

Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.

A. Key rotation improves encryption efficiency
B. Key rotation reduces risk if a key is ever compromised
C. Periodically rotating keys makes it easier to reuse keys
D. Periodically rotating keys limits the amount of data encrypted by one key version
E. Key rotation allows you to encrypt no more than five keys at a time

Question 4

Which of the following analytical and statistical techniques do data scientists commonly use?

A. Regression
B. Clustering
C. Classification
D. All of the above

Question 5

Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?

A. Data Modeling
B. Data Monitoring
C. Data Access
D. Data Exploration

Solutions: