Vishal Parikh vdparikh

Istio is a powerful service mesh that provides a way to control, secure, and observe microservices in a Kubernetes environment. It works by injecting a sidecar proxy (Envoy) alongside each service, which handles communication between services. Istio provides features like traffic management, security (mTLS, RBAC), and observability (metrics, logs, tracing).

1. Key Concepts of Istio

Before diving into code, let’s understand the key components of Istio:

Sidecar Proxy (Envoy):

Injected into each pod in your Kubernetes cluster.

Building a Key Management System (KMS) on top of an on-premises Hardware Security Module (HSM) involves creating a software layer that interacts with the HSM to provide key lifecycle management and cryptographic operations. This KMS can then expose APIs or SDKs for other services to use. Here’s what it takes to build such a system:

1. Understand HSM Capabilities

HSMs provide hardware-backed secure key storage and cryptographic operations. Before building the KMS, understand the following features of your HSM:

Key Storage: Maximum number of keys, supported key types (e.g., RSA, AES, ECC).
Cryptographic Operations: Supported algorithms (e.g., encryption, signing, HMAC).
APIs or SDKs: Interfaces provided for interacting with the HSM (e.g., PKCS#11, JCE, KMIP, proprietary APIs).
Access Control: User roles, permissions, and multi-tenancy support.

Imagine you are designing a simple system for an insurance company to manage their insurance policies. Each insurance policy has the following attributes:

Policy ID (a unique identifier)
Policyholder Name
Policy Start Date
Policy End Date
Premium Amount

Your task is to create a simple API or a command-line program in a programming language of your choice (e.g., Python, Java, C#) to manage these insurance policies. You need to implement the following functionalities:

The following coding problem requires Senior engineer level or up to finish two questions within 50 minutes.

We are working on a security system for a badged-access room in our company's building.

Given an ordered list of employees who used their badge to enter or exit the room, write a function that returns two collections: a. All employees who didn't use their badge while exiting the room – they recorded an enter without a matching exit. b. All employees who didn't use their badge while entering the room – they recorded an exit without a matching enter.

badge_records = [
 ["Martha", "exit"],

	package main

	import (
	"crypto/rand"
	"encoding/base64"
	"fmt"
	"log"
	"os"
	"strings"

	import os
	import fnmatch
	import pandas as pd
	from presidio_analyzer import AnalyzerEngine, RecognizerResult, PatternRecognizer, Pattern
	from presidio_anonymizer import AnonymizerEngine
	from presidio_anonymizer.entities import EngineResult
	from presidio_image_redactor import ImageRedactorEngine, ImageAnalyzerEngine
	from PIL import Image
	import docx2txt
	from pdf2image import convert_from_path

	using Microsoft.AspNetCore.Builder;
	using Microsoft.AspNetCore.Hosting;
	using Microsoft.AspNetCore.Http;
	using Microsoft.Extensions.DependencyInjection;
	using Microsoft.Extensions.Hosting;
	using Newtonsoft.Json;
	using System;
	using System.Collections.Concurrent;
	using System.Collections.Generic;
	using System.Threading.Tasks;

	wget https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin


	pip install pyllama
	mkdir llama
	python -m llama.download --model_size 7B --folder llama/


	# https://github.com/nomic-ai/pygpt4all/pyllamacpp
	# pip install pyllamacpp fails and so directly download it from github