AI-900: How Does Data Cleaning Impact Machine Learning Model Accuracy?

Learn how mastering data cleaning can improve machine learning outcomes and help you excel in the AI-900 Azure AI Fundamentals exam preparation.

Question

Which step does a data scientist need to perform to create the dataset for modeling?

A. Tune the model
B. Clean the data
C. Deploy the model
D. Explore the data

Answer

B. Clean the data

Explanation

Cleaning the data needs to be performed by a data scientist to create the dataset for modeling. This involves removing errors, inconsistencies, and missing values, which is crucial for preparing the data for modeling. It ensures that the model is trained in accurate and complete information. Data science processes have the following outline:

1. Define the Business Problem – Collaborate with stakeholders to clearly define the problem, objectives, and solution requirements.
2. Define the Analytic Approach – Choose an analytic approach based on the business problem.
3. Obtain the Data – Identify and acquire the necessary data from various sources. This includes querying databases, extracting information from websites (web scraping), obtaining data from files, purchasing data if required, and collecting new data if necessary.
4. Clean the Data (Scrubbing) – This involves converting data into a consistent format, organizing data, removing unnecessary information, and replacing missing data.
5. Explore the Data – This involves exploring cleaned data using statistical analytical techniques and revealing relationships between data features.
6. Model the Data – This involves building and training prescriptive or descriptive models and testing and evaluating the model’s performance.
7. Deploy the Model – This involves delivering the final model with documentation and deploying the new dataset to production after thorough testing.
8. Visualize and Communicate Results – This involves using visualization tools (e.g., Microsoft Power BI, Tableau, Apache Superset, Metabase) for data exploration.

Deploying and tuning the model happen after the dataset has been finalized and the model built.

Exploring the data involves analyzing its characteristics and relationships but does not necessarily involve cleaning it.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.