Blackstraw AI Interview Questions, Process, and Tips for Experienced

Databinding in Android allows for easier connection between UI components and data sources.

• Databinding eliminates the need for findViewById() calls in your code.

• It allows for easier access to data in your layouts using data binding expressions.

• Databinding can improve code readability and reduce boilerplate code.

• Example:

Packaging process in Android involves compiling the code, resources, and assets into an APK file for distribution.

• Compile the Java code into .class files

• Compile the resources (XML files, images, etc.) into a binary format

• Package all the compiled files into an APK file using the Android Asset Packaging Tool (AAPT)

• Sign the APK file with a private key for security

• Align the APK file for optimization

MVVM is a design pattern that separates the user interface from the business logic and data model.

• MVVM stands for Model-View-ViewModel

• Model represents the data and business logic

• View represents the user interface

• ViewModel acts as an intermediary between the Model and View

• MVVM helps in achieving separation of concerns and easier unit testing

Cardinality refers to the uniqueness of values in a column or set of columns in a database table.

• Cardinality is the number of unique values in a column or set of columns.

• High cardinality means a column has many unique values, while low cardinality means few unique values.

• For example, a column like 'employee_id' would have high cardinality, while a column like 'gender' would have low cardinality.

ML algorithm selection is based on data characteristics, problem type, and desired outcomes.

• Understand the problem type (classification, regression, clustering, etc.)

• Consider the size and quality of the data

• Evaluate the complexity of the model and interpretability requirements

• Choose algorithms based on their strengths and weaknesses for the specific task

• Experiment with multiple algorithms and compare their performance

• F...read more

To optimize a ML model, one can tune hyperparameters, feature engineering, cross-validation, ensemble methods, and regularization techniques.

• Tune hyperparameters using techniques like grid search or random search

• Perform feature engineering to create new features or select relevant features

• Utilize cross-validation to evaluate model performance and prevent overfitting

• Explore ensemble methods like bagging and boosting to ...read more