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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

1. Which OCI service enables you to build, train, and deploy machine learning models in the cloud?

A) Oracle Cloud Infrastructure Data Catalog
B) Oracle Cloud Infrastructure Data Integration
C) Oracle Cloud Infrastructure Data Science
D) Oracle Cloud Infrastructure Data Flow

2. In machine learning, what is the primary difference between supervised and unsupervised learning?

A) Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
B) Supervised learning is monitored closely by data scientists, while they don’t play a role inunsupervised learning.
C) Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.
D) Supervised learning is created and managed by the Data Engineer.

3. You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (ADS) SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which two should you look for to ensure permissions are set up correctly?

A) The model artifact is saved to the block volume of the notebook session
B) The networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway
C) The policy for a dynamic group grants manage permissions for the model catalog in this compartment
D) A dynamic group has rules that match the notebook sessions in its compartment
E) The policy for your user group grants manage permissions for the model catalog in this compartment

4. You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?

A) Mobile App
B) The OCI Software Development Kit (SDK)
C) OCI Console
D) Command Line Interface (CLI)

5. You have received machine learning model training code, without clear information about the optimal shape to run the training on. 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 a small 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 rerun the job. Repeat the process until the processing time does not improve.
B) Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimization and performance tests in advance to identify the right compute shape before running the model training as a job.
C) 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 rerun the job. Repeat the process until the shape resources are fully utilized.
D) Start with the strongest compute shape Jobs 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.

Solutions: