Name: Oracle Cloud Infrastructure Data Science 2023 Professional
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Free Oracle 1Z0-1110-23 Exam Actual Questions

The questions for 1Z0-1110-23 were last updated On May 7, 2025

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Question No. 1

During a job run, you receive an error message that no space is left on your disk device. To solve

the problem, you must increase the size of the job storage. What would be the most efficient way to

do this with Data Science Jobs?

ACreate a new job with increased storage size and then run the job.

BOn the job run, set the environment variable that helps increase the size-of the storage.

CYour code is using too much disk space. Refactor the code to identify the problem.

DEdit the job, change the size of the storage of your job, and start a new job run.

Show Answer

Correct Answer: D

Question No. 2

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?

AThe current best-known hyperparameter configuration is returned.

BA random hyperparameter configuration is returned.

CA hyperparameter configuration with a minimum learning rate is returned.

DThe last generated hyperparameter configuration is returned

Show Answer

Correct Answer: A

Question No. 3

As a data scientist, you are tasked with creating a model training job that is expected to take

different hyperparameter values on every run. What is the most efficient way to set those

parameters with Oracle Data Science Jobs?

ACreate a new job every time you need to run your code and pass the parameters as
environment variables.

BCreate a new job by setting the required parameters in your code and create a new job for
every code change.

CCreate 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.

DCreate your code to expect different parameters as command line arguments and create a
new job every time you run the code.

Show Answer

Correct Answer: C

Question No. 4

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?

AStart 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.

BStart 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.

CStart 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.

DStart 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.

Show Answer

Correct Answer: D

Question No. 5

You are a data scientist designing an air traffic control model, and you choose to leverage Oracle

AutoML You understand that the Oracle AutoML pipeline consists of multiple stages and

automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML

pipeline?

AAlgorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
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BAdaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning

CAdaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning

DAlgorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning

Show Answer

Correct Answer: B