Selected 1z0-1110-23 Oracle Cloud Infrastructure Certification Questions Answers

Principal Component Analysis

Multiclass Classification

K-means Clustering

Recurrent Neural Network

Binary Classification

Regression Analysis

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.

Create the job in Data Science Jobs and then start the number of simultaneous jobs runs

required for your workload.

Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel

workloads.

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.

ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies

the_main_ top level where the code is run.

Rewrite your code so that it is a single executable Python or Bash/Shell script file.

ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs

identifies the main executable file automatically.

ZIP the entire code project folder, upload it as a Job artifact on job creation, and set

JOB_RUN_ENTRYPOINT to point to the main executable file.

Create a new job every time you need to run your code and pass the parameters as

environment variables.

Create a new job by setting the required parameters in your code and create a new job for

every code change.

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.

Create your code to expect different parameters as command line arguments and create a

new job every time you run the code.