1Z0-1110-25 Exam Details

  • Exam Code
    :1Z0-1110-25
  • Exam Name
    :Oracle Cloud Infrastructure 2025 Data Science Professional
  • Certification
    :Oracle Certifications
  • Vendor
    :Oracle
  • Total Questions
    :158 Q&As
  • Last Updated
    :May 29, 2026

Oracle 1Z0-1110-25 Online Questions & Answers

  • Question 31:

    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, and copy your files and data from your temporary bucket onto your new notebook session
    B. 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
    C. 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
    D. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re-create all of your file changes

  • Question 32:

    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 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.
    B. 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.
    C. 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.
    D. 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.

  • Question 33:

    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

  • Question 34:

    Where do calls to stdout and stderr from score.py go in a model deployment?

    A. The file that was defined for them on the Virtual Machine (VM)
    B. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
    C. The OCI Cloud Shell, which can be accessed from the console
    D. The OCI console

  • Question 35:

    The feature type TechJob has the following registered validators:

    TechJob.validator.register(name='is_tech_job',

    handler=is_tech_job_default_handler)

    TechJob.validator.register(name='is_tech_job',

    handler=is_tech_job_open_handler, condition=('job_family',)) TechJob.validator.register(name='is_tech_job',

    handler=is_tech_job_closed_handler, condition=('job_family': 'IT'))

    When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?

    A. Execute the is_tech_job_default_handler handler
    B. Throw an error because the system cannot determine which handler to run
    C. Execute the is_tech_job_closed_handler handler
    D. Execute the is_tech_job_open_handler handler

  • Question 36:

    You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?

    A. Feature Permutation Importance Explanations
    B. Local Interpretable Model-Agnostic Explanations
    C. Feature Dependence Explanations
    D. Accumulated Local Effects

  • Question 37:

    What is the primary difference between a data scientist and a data engineer?

    A. A data engineer collects and prepares data, and a data scientist then analyzes it.
    B. A data engineer analyzes data after a data scientist collects and prepares it.
    C. A data engineer builds data pipelines and helps prepare data, while a data scientist is responsible for data collection, preparation, and analysis.
    D. A data engineer creates data flows to be used as templates by the data analyst.

  • Question 38:

    Which Oracle Cloud Service provides restricted access to target resources?

    A. Bastion
    B. Internet Gateway
    C. Load Balancer
    D. SSL Certificate

  • Question 39:

    Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?

    A. Jobs provisions the infrastructure to run a process on-demand
    B. Jobs comes with a set of standard tasks that cannot be customized
    C. You must create and manage your own Jobs infrastructure
    D. You must use a single Shell/Bash or Python artifact to run a job

  • Question 40:

    You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?

    A. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
    B. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session
    C. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
    D. Launch a notebook session, install a PySpark conda environment, configure core- site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK

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