When resolving a merge conflict in Git, what approach would you take to ensure the integrity of the codebase?
- Choose one side's changes without review
- Merge conflicting changes manually
- Discard conflicting changes
- Always accept remote changes
The correct option is b) Merge conflicting changes manually. This approach involves carefully reviewing and selecting the changes from both sides to create a resolution that maintains code integrity. Options a, c, and d may lead to loss of important code or introduce errors.
In a situation where data normalization is causing performance issues, what SQL technique could be used to balance normalization with query efficiency?
- Denormalization
- Indexing
- Partitioning
- Subquery
In situations where data normalization impacts performance, denormalization can be used. Denormalization involves introducing redundancy into the database by storing redundant data, which can reduce the need for complex joins and improve query efficiency. It's a trade-off between normalization and performance.
In financial time series data of a stock market, what type of model would be ideal for predicting future stock prices considering past trends and volatilities?
- Autoregressive Integrated Moving Average (ARIMA)
- GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
- Long Short-Term Memory (LSTM) Networks
- Random Forest Regressor
GARCH models are well-suited for financial time series data as they account for volatility clustering and changing variances over time. ARIMA and LSTM are more focused on capturing patterns in the mean, while Random Forest is generally not used for time series forecasting in financial markets.
In a binary search algorithm, what is the time complexity for searching an element in a sorted array of n elements?
- O(1)
- O(log n)
- O(n)
- O(n^2)
The time complexity of a binary search algorithm is O(log n), as it repeatedly divides the search interval in half, resulting in a logarithmic time complexity. This makes it more efficient than linear search algorithms (O(n)).
How does a NoSQL database differ from a traditional SQL database?
- NoSQL databases are limited to a single data model.
- NoSQL databases are schema-less, allowing for flexible and dynamic data models.
- SQL databases are only suitable for small-scale applications.
- SQL databases use a key-value pair storage mechanism.
NoSQL databases provide a flexible and dynamic data model, allowing for schema-less data storage. This contrasts with traditional SQL databases, which follow a structured, tabular format with a fixed schema.
For real-time data processing, ETL uses ________ to handle streaming data.
- Apache Kafka
- Hadoop
- MongoDB
- SQL Server
Apache Kafka is commonly used in ETL processes for real-time data processing. It is a distributed event streaming platform that excels at handling high-throughput, fault-tolerant, and scalable data streams, making it suitable for managing streaming data in ETL pipelines.
A _______ tree is a data structure that allows fast search, insert, delete, and nearest-neighbor operations.
- AVL
- B-Tree
- Heap
- Trie
A B-Tree is a self-balancing tree data structure that allows for efficient search, insert, delete, and nearest-neighbor operations. It is commonly used in databases and file systems for its balanced nature, ensuring consistent performance.
When you need to create a lagged feature in a time series dataset in Pandas, which function would you use?
- delay()
- diff()
- lag()
- shift()
The shift() function in Pandas is used to create lagged features in a time series dataset. It shifts the values of a column by a specified number of periods, allowing you to create lagged versions of the original data for time series analysis.
In time series analysis, how is the term 'stationarity' best described?
- The ability of a time series to move in a straight line
- The predictability of future values in a time series
- The presence of external factors affecting a time series
- The statistical properties of a time series remaining constant over time
Stationarity refers to the statistical properties of a time series remaining constant over time. Achieving stationarity is important for accurate modeling and forecasting in time series analysis.
In data warehousing, what does ETL stand for?
- Efficient Transactional Logic
- Export, Transform, Load
- Extract, Transfer, Load
- Extract, Transform, Load
ETL stands for Extract, Transform, Load. It is a process used in data warehousing to extract data from source systems, transform it into a usable format, and then load it into a data warehouse for analysis and reporting.