1z0-1110-23 무료 덤프문제 온라인 액세스

You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many subfolders and classes.
What is the best way to run this project as a Job?

• A.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.
• B.ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically.
• C.Rewrite your code so that it is a single executable Python or Bash/Shell script file.
• D.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.

As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload.
How would you scale up your notebook session without losing your work?

• A.Create a temporary bucket on Object Storage, write all your files and data to Object Storage, delete your notebook session, provision a new notebook session on a larger compute shape, Want any exam dump in pdf email me at [email protected] (Little Paid) and copy your files and data from your temporary bucket onto your new notebook session.
• B.Deactivate your notebook session, provision a new notebook session on a larger compute shape and re-create all of your file changes.
• C.Ensure your files and environments are written to the block volume storage under the
  /home/datascience directory, deactivate the notebook session, and activate the notebook session with a larger compute shape selected.
• D.Download all your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session.

As a data scientist, you create models for cancer prediction based on mammographic images.
The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix.
Model 1 has Test accuracy is 80% and recall is 70%.
* Model 2 has Test accuracy is 75% and recall is 85%.
Which model would you prefer and why?

• A.Model 2, because recall is high.
• B.Model 1, because recall has lesser impact on predictions in this use case
• C.Model 1, because the test accuracy is high.
• D.Model 2, because recall has more impact on predictions in this use se.

You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

• A.Start with the strongest compute shape Job's support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost.
• B.Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and re-run the job. Repeat the process until the processing time does not improve.
• C.Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job.
• D.Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and re- run the job. Repeat the process until the shape resources are fully utilized.

For your next data science project, you need access to public geospatial images.
Which Oracle Cloud service provides free access to those images?

• A.Oracle Open Data
• B.Oracle Big Data Service
• C.Oracle Analytics Cloud
• D.Oracle Cloud Infrastructure Data Science