Which method for handling missing data involves using algorithms like k-NN to find similar records to impute the missing value?
- Mean imputation
- Median imputation
- k-NN imputation
- Mode imputation
k-NN imputation is a technique that uses the similarity of data points to impute missing values. It finds records with similar characteristics to the one with missing data and replaces the missing value with the imputed value from its nearest neighbors. Other options are simpler imputation methods.
In recurrent neural networks (RNNs), which variant is designed specifically to handle long-term dependencies by maintaining a cell state?
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- SRU (Simple Recurrent Unit)
- ESN (Echo State Network)
Long Short-Term Memory (LSTM) is a variant of RNN designed to handle long-term dependencies by maintaining a cell state that can capture information over long sequences. LSTM's ability to store and retrieve information over extended time steps makes it well-suited for tasks involving long-term dependencies in data sequences.
Which metric provides a single score that balances the trade-off between precision and recall?
- F1 Score
- Accuracy
- ROC AUC
- Log Loss
The F1 Score is a metric that balances the trade-off between precision and recall. It is especially useful when dealing with imbalanced datasets or when you want to find a balance between correctly identifying positive cases (precision) and capturing all positive cases (recall). The F1 Score is the harmonic mean of precision and recall. It is a suitable choice for evaluating models when both precision and recall are essential.
An AI startup with limited computational resources is building an image classifier. They don't have the capability to train a deep neural network from scratch. What approach can they use to leverage the capabilities of deep learning without the extensive training time?
- Transfer learning
- Reinforcement learning
- Genetic algorithms
- Random forest classifier
Transfer learning allows the startup to use pre-trained deep neural networks (e.g., a pre-trained CNN) as a starting point. This approach significantly reduces training time and computational resources, while still benefiting from the capabilities of deep learning.
A common architecture for real-time data processing involves using ________ to ingest and process streaming data.
- Hadoop
- Spark
- Batch Processing
- Data Lakes
In real-time data processing, Apache Spark is commonly used to ingest and process streaming data. Spark provides the capabilities to handle streaming data in real time, making it a popular choice for such applications.
In a skewed distribution, which measure of central tendency is most resistant to the effects of outliers?
- Mean
- Median
- Mode
- Geometric Mean
The median is the most resistant measure of central tendency in a skewed distribution. It is less affected by extreme values or outliers since it represents the middle value when the data is arranged in order. The mean, mode, and geometric mean can be heavily influenced by outliers, causing them to be less representative of the data's central location.
What is a common technique to prevent overfitting in linear regression models?
- Increasing the model complexity
- Reducing the number of features
- Regularization
- Using a smaller training dataset
Regularization is a common technique used to prevent overfitting in linear regression models. It adds a penalty term to the linear regression's cost function to discourage overly complex models. Regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.
In which type of data do you often encounter a mix of structured tables and unstructured text?
- Structured Data
- Semi-Structured Data
- Unstructured Data
- Multivariate Data
Semi-structured data often contains a mix of structured tables and unstructured text. It's a flexible data format that can combine organized data elements with more free-form content, making it suitable for a wide range of data types and use cases, such as web data and NoSQL databases.
Which technique considers the spread of data points around the median to identify outliers?
- Box Plot
- Z-Score (Standardization)
- One-Hot Encoding
- K-Means Clustering
The Box Plot, also known as a box-and-whisker plot, considers the spread of data points around the median and helps identify outliers based on the interquartile range (IQR). Outliers are data points that fall outside the whiskers of the box plot. Z-Score is used for standardization, One-Hot Encoding is used for categorical variables, and K-Means Clustering is a clustering technique and not used for identifying outliers.
In Big Data processing, _______ operations filter and sort data, while _______ operations perform aggregations and transformations.
- Map, Reduce
- Filter, Join
- Shuffle, Merge
- Merge, Filter
In Big Data processing, the first blank should be filled with "Filter," and the second blank with "Join." Filtering and sorting are common operations in data preparation, while aggregations and transformations are typically done using join operations.