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    HomeCertificationsOracle Cloud Infrastructure 2025 Data Science ProfessionalFree Practice Test
    Prasenjit Sarkar
    By Prasenjit Sarkar·Last verified: 2026-05-22
    Oracle FreePROFESSIONAL

    Free Oracle Cloud Infrastructure 2025 Data Science Professional Practice Test

    1Z0-1110-25

    Test your knowledge with 20 free practice questions for the 1Z0-1110-25 exam. Get instant feedback and see if you are ready for the real exam.

    100% Free — No credit card required
    Takes only 10–15 minutes
    Instant answers with explanations
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    Test Overview

    Questions20
    Time LimitNo Limit
    DifficultyPROFESSIONAL
    PriceFREE

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    Free Questions

    Sample Practice Questions

    Try these Oracle Cloud Infrastructure 2025 Data Science Professional sample questions — no signup required

    Sample 20 Free
    1
    OCI Data Science Service

    A data scientist needs to set up a development environment in OCI Data Science service to work on multiple machine learning projects. Which component provides an interactive coding environment with pre-installed libraries and frameworks?

    2
    OCI Data Science Service

    Your organization requires that all data science workloads comply with specific security policies including network isolation and restricted access. Which OCI networking feature should you configure for your Data Science notebook sessions?

    3
    OCI Data Science Service

    A data science team needs to train a deep learning model that requires GPU acceleration. The training job will take several hours and should not be interrupted if a team member closes their laptop. What is the BEST approach?

    4
    OCI Data Science Service

    You need to version control your machine learning models and track metadata such as training metrics, hyperparameters, and model artifacts. Which OCI Data Science feature should you use?

    5
    OCI Data Science Service

    Your team has developed a custom conda environment with specific library versions required for a machine learning project. How can you ensure this environment is available across multiple notebook sessions and job runs?

    6
    OCI Data Science Service

    A machine learning project requires accessing data from multiple sources including Object Storage, Autonomous Database, and external APIs. What is the recommended way to manage credentials and connection information securely in OCI Data Science?

    7
    Machine Learning Model Development

    During model development, you need to handle class imbalance in a binary classification problem where the positive class represents only 2% of the dataset. Which technique would be MOST effective?

    8
    Machine Learning Model Development

    You are developing a regression model and notice high variance between training and validation performance. The training error is very low but validation error is high. What should you do?

    9
    Machine Learning Model Development

    A data scientist needs to perform hyperparameter tuning for a complex machine learning model with multiple hyperparameters. Which approach provides the best balance between exploration and computational efficiency?

    10
    Machine Learning Model Development

    You are building a time series forecasting model for retail sales prediction. Which validation strategy is MOST appropriate to avoid data leakage?

    11
    Machine Learning Model Development

    When developing a natural language processing model for sentiment analysis, you need to convert text data into numerical representations. Which technique would capture semantic meaning and context better than simple bag-of-words?

    12
    Model Deployment and Management

    You have deployed a machine learning model in OCI Data Science as a model deployment. The model needs to handle varying traffic patterns with occasional spikes. What configuration should you implement?

    13
    Model Deployment and Management

    Your deployed model needs to provide predictions with sub-second latency for a real-time application. Which deployment type in OCI Data Science is MOST appropriate?

    14
    Model Deployment and Management

    After deploying a model to production, you notice that prediction accuracy has degraded over time. Which strategy should you implement to detect and address this issue?

    15
    Model Deployment and Management

    You need to deploy multiple versions of a model simultaneously to perform A/B testing and gradually shift traffic from the old version to the new version. What deployment strategy should you use?

    16
    Model Deployment and Management

    Your organization requires that all model predictions be logged for audit and compliance purposes. What should you configure in your OCI Data Science model deployment?

    17
    Data Engineering and MLOps

    You are setting up a data pipeline to prepare training data for machine learning models. The raw data in Object Storage needs to be cleaned, transformed, and stored in a format optimized for model training. Which OCI service combination is MOST appropriate?

    18
    Data Engineering and MLOps

    Your MLOps pipeline needs to automatically retrain and deploy models when new training data becomes available. Which components should you include in your automation workflow?

    19
    Data Engineering and MLOps

    You need to track experiments including code versions, parameters, metrics, and artifacts across multiple model training runs. What approach should you implement?

    20
    Data Engineering and MLOps

    Your machine learning pipeline processes sensitive customer data and must comply with data governance policies including data lineage tracking and access controls. Which combination of OCI services addresses these requirements?

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