RAG (Retrieval-Augmented Generation) is an architecture that combines retrieval-based and generation-based approaches to improve the accuracy, factuality and context-awareness of large language models (LLMs). LangGraph is a framework built on top of LangChain for developing stateful, graph-based agentic workflows. Embedding databases (also called vector databases) are specialized databases designed to store, index, and retrieve vector embeddings generated by AI models. Tokenization is the process of dividing text into smaller, meaningful units called tokens which can be words, subwords or characters, depending on the model’s design. Positional encoding allows the model to capture sequence https://uvik.io/ structure and relative positions of elements.

• It allows to generate key-value pairs in a single line of code, making the code more readable and efficient.
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• Discuss algorithms, distributed considerations, and implementation.
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• Primarily, it excels in end-to-end testing, allowing users to test the complete functionality of web applications from the user’s perspective.

Dictionary is a collection of key-value pairs used to store and retrieve data using unique keys. In the second example, strings are immutable, so concatenating ” World” creates a new string object and assigns it back to s. First, Python source code (.py files) is compiled into bytecode (.pyc files). It showcased the power of first-class functions in creating modular and extensible code.” By leveraging first-class functions, we created a library of transformation functions, each addressing a specific aspect of the data manipulation.
A Star Schema consists of one large “Fact” table (containing quantitative data like sales amounts) connected to several “Dimension” tables (containing descriptive data like product names or dates). You use it when your data is so large that it cannot fit on a single machine. Staging tables act as a temporary landing zone for raw data before it is transformed. In Python pipelines, lineage is often tracked by logging metadata at every step or using specialized tools like OpenLineage that integrate with your code to automatically capture the movement of data. These tools allow you to define complex dependencies (e.g., don’t run Job B until Job A succeeds), provide retry logic, and offer a UI to monitor the health of all your data flows. Idempotency allows you to simply “re-run” a failed job without having to manually clean up the database first.

Scenario based

In a snowflake schema, the fact table is placed at the center and connected to dimension tables, which are further normalized into sub-dimension tables. A snowflake schema is a type of data warehouse schema where dimension tables are normalized into multiple related tables, forming a hierarchical structure. The dimension tables store descriptive information such as product details, customer information, or time data, which provide context to the facts. This fact table is connected to multiple dimension tables through foreign key relationships. The schema helps establish how data is integrated and stored for optimized querying and reporting in the data warehouse environment.
By default, Playwright auto-dismisses any unhandled dialog so the test does not block. The fixtures handle setup and teardown automatically. This question separates candidates who memorized POM from candidates who actually built a framework. Walk-through-your-framework lives here, and the gap between candidates who memorized POM and candidates who shipped one is obvious in 30 seconds. This is the single most important pattern to internalize for stable tests.
Interview questions typically center around base language skills and standard library functionality. If your interview IDE does offer extra features, view them as an added bonus. However, interview coding environments are generally more lightweight, intentionally limiting available features to concentrate on assessing coding abilities.
One dimensional array capable of storing different data types is called a series. Python comprehensions, like decorators, are syntactic sugar constructs that help build altered and filtered lists, dictionaries, or sets from a given list, dictionary, or set. Decorators in Python are essentially functions that add functionality to an existing function in Python without changing the structure of the function itself. The key difference between the two is that while lists are mutable, tuples on the other hand are immutable objects.