CML API call to start the AIML job

1) When is V1 getting depreciated?
it is already
https://docs.cloudera.com/machine-learning/cloud/jobs-pipelines/topics/ml-rest-apis.html
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The Jobs API is now deprecated. See Cloudera AI API v2 and API v2 usage for the successor API.
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Is it recommended to use V1 API now? 
Obviously, No.

2) Which API method is to be used in the Version 2 API call to start the job?
https://docs.cloudera.com/machine-learning/1.5.4/rest-api-reference/index.html#api-CMLService-createJobRun

may be createjob, createjobrun are confusing

You can find the Docs for Python and API by going to User Settings -> API Keys

From the Python CML APIv2 Docs, these are the two methods you need:

Step 1: Create Job 

from __future__ import print_function
import time
import cmlapi
from cmlapi.rest import ApiException
from pprint import pprint

# create an instance of the API class
api_instance = cmlapi.CMLServiceApi()
body = cmlapi.CreateJobRequest() # CreateJobRequest |
project_id = 'project_id_example' # str | ID of the project containing the job.

try:
  # Create a new job.
  api_response = api_instance.create_job(body, project_id)
  pprint(api_response)
except ApiException as e:
  print("Exception when calling CMLServiceApi->create_job: %s\n" % e)

Step 2: Run the Job

from __future__ import print_function
import time
import cmlapi
from cmlapi.rest import ApiException
from pprint import pprint

# create an instance of the API class
api_instance = cmlapi.CMLServiceApi()
body = cmlapi.CreateJobRunRequest() # CreateJobRunRequest | 
project_id = 'project_id_example' # str | ID of the project containing the job.
job_id = 'job_id_example' # str | The job ID to create a new job run for.

try:
    # Create and start a new job run for a job.
    api_response = api_instance.create_job_run(body, project_id, job_id)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling CMLServiceApi->create_job_run: %s\n" % e)

Using the "cmlapi.CreateJobRequest()" or "cmlapi.CreateJobRunRequest()" methods can be tricky. Here's an advanced example: https://github.com/pdefusco/SparkGen/blob/main/autogen/cml_orchestrator.py

In particular:

sparkgen_1_job_body = cmlapi.CreateJobRequest(
    project_id = project_id,
    name = "SPARKGEN_1_"+session_id,
    script = "autogen/cml_sparkjob_1.py",
    cpu = 4.0,
    memory = 8.0,
    runtime_identifier = "docker.repository.cloudera.com/cloudera/cdsw/ml-runtime-workbench-python3.7-standard:2023.05.1-b4",
    runtime_addon_identifiers = ["spark320-18-hf4"],
    environment = {
                    "x":str(x),
                    "y":str(y),
                    "z":str(z),
                    "ROW_COUNT_car_installs":str(ROW_COUNT_car_installs),
                    "UNIQUE_VALS_car_installs":str(UNIQUE_VALS_car_installs),
                    "PARTITIONS_NUM_car_installs":str(PARTITIONS_NUM_car_installs),
                    "ROW_COUNT_car_sales":str(ROW_COUNT_car_sales),
                    "UNIQUE_VALS_car_sales":str(UNIQUE_VALS_car_sales),
                    "PARTITIONS_NUM_car_sales":str(PARTITIONS_NUM_car_sales),
                    "ROW_COUNT_customer_data":str(ROW_COUNT_customer_data),
                    "UNIQUE_VALS_customer_data":str(UNIQUE_VALS_customer_data),
                    "PARTITIONS_NUM_customer_data":str(PARTITIONS_NUM_customer_data),
                    "ROW_COUNT_factory_data":str(ROW_COUNT_factory_data),
                    "UNIQUE_VALS_factory_data":str(UNIQUE_VALS_factory_data),
                    "PARTITIONS_NUM_factory_data":str(PARTITIONS_NUM_factory_data),
                    "ROW_COUNT_geo_data":str(ROW_COUNT_geo_data),
                    "UNIQUE_VALS_geo_data":str(UNIQUE_VALS_geo_data),
                    "PARTITIONS_NUM_geo_data":str(PARTITIONS_NUM_geo_data)
                    }
)
sparkgen_1_job = client.create_job(sparkgen_1_job_body, project_id)

And

jobrun_body = cmlapi.CreateJobRunRequest(project_id, sparkgen_1_job.id)
job_run = client.create_job_run(jobrun_body, project_id, sparkgen_1_job.id)

Hope this helps,