Free Oracle Cloud Infrastructure 2025 Data Science ProfessionalPractice Test
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.
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Free Practice Questions
Try these Oracle Cloud Infrastructure 2025 Data Science Professional sample questions for free - no signup required
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?
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?
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?
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?
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?
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?
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?
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?
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?
You are building a time series forecasting model for retail sales prediction. Which validation strategy is MOST appropriate to avoid data leakage?
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?
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?
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?
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?
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?
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?
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?
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?
You need to track experiments including code versions, parameters, metrics, and artifacts across multiple model training runs. What approach should you implement?
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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