Python

How do you find the described scope of work and tasks?

What else needs to be considered besides the log fragment to analyze incidents for multiple clients over a long period of time?

Isolation levels Existing isolation levels (standards) READ_UNCOMMITTED READ_COMMITTED REPEATABLE_READ SERIALIZABLE Task There are 3 entities: * Entity1 * Entity2 * Entity3 Database postgres Task What isolation level is needed for both transactions to guarantee no data loss in these scenarios and why: Scenario 1 (corresponds to execution time) * Open transaction 1; * select Entity1; * select Entity2; * Open transaction 2; * select Entity1; * update Entity1; ********** * Commit transaction 2; * update Entity1; ->transaction 1 * update Entity2; ->transaction 1

What infrastructure and architectural patterns should be considered in a Python service to integrate it into an enterprise microservice infrastructure, support multiple clients, fault diagnosis, and operation?

How to diagnose a situation when a client sent a screenshot of a 'white screen' and reported that the application is not working?

How to foresee the integration of Python application logs with different clients' logging systems without rebuilding the application?

How to trace a request from the first service to yours in a chain of microservices?

Given two vectors, size N. Each of N users is described by a set of numerical features — vectors of dimension M. Task: find which users are most similar to each other.

Have you encountered PostgreSQL optimization and how would you optimize database performance?

How is it more convenient to deliver a Python application to a client for deployment, and what are the alternatives to a Docker image?

CREATE TABLE public.ntik_model_case ( case_id serial4 NOT NULL, parent_case_id int4 NULL, CONSTRAINT ntik_model_case_pkey PRIMARY KEY (case_id), CONSTRAINT ntik_model_case_parent_case_fkey FOREIGN KEY (parent_case_id) REFERENCES public.ntik_model_case(case_id) );

What types of PostgreSQL indexes have you used and what are their features?

How to efficiently work with a sparse interaction matrix where there are many zeros, so as not to consider empty values and not to waste extra memory?

What are the features of a B-tree index in PostgreSQL?

1. There is a social network: 1. Users - the top of the graph 2. Interaction possibilities (friendship) with other users (like, comment, message, repost) = edge 3. Mutual connections (e.g., number of interactions) = edge weight 2. Tasks: 1. Find the most active user 2. Find the most popular user import numpy as np # shape = (n, n) # adjacency shape = (n, n) example = np.array([ [0, 8, 3, 0, 2], # Alice [2, 0, 10, 0, 0], # Bob [4, 2, 0, 7, 1], # Charlie [0, 0, 6, 0, 0], # John [3, 1, 2, 0, 0], # Enea ])

How do applications in microservice architecture find out each other's current addresses?

What data must be logged when an error or exception occurs?