Top 8 ChatGPT prompts to turn job interviews into job offers 

Answer tough questions with ease. Impress interviewers!

Use these proven ChatGPT prompts:

🎯 Prompt 1: Job Description Analyzer

Analyze the job description for [Position]. Identify the top 5 required skills and responsibilities. Create a table matching these to my experiences. Suggest 3 unique talking points that align with the role. My resume: [Paste Resume]. Job description: [Paste Job Description].

🎯 Prompt 2: Company Research Synthesizer

Research [Company Name]. Summarize their mission, recent achievements, and industry position. Create 5 talking points about how my skills align with their goals. Suggest 2 insightful questions about their future plans. Company website: [Website URL].

🎯 Prompt 3: Challenging Situation Navigator

Prepare responses for 3 difficult scenarios common in [Job Title]: a conflict with a colleague, a project failure, and a tight deadline. For each, create a structured answer using the STAR method, emphasizing problem-solving and learning outcomes. Include key phrases that showcase my resilience and adaptability. Limit each response to 100 words. My resume: [Paste Resume]. Job description: [Paste Job Description].

🎯 Prompt 4: Common Question Response Generator

Prepare answers for 5 common interview questions for [Job Title]. Use a mix of professional accomplishments and personal insights. Keep each answer under 2 minutes when spoken. Provide a key point to emphasize for each answer. My resume: [Paste Resume].

🎯 Prompt 5: STAR Method Response Builder

Develop 3 STAR method responses for likely behavioral questions in [Industry]. Focus on problem-solving, leadership, and teamwork scenarios. Provide a framework to adapt these stories to different questions. My resume: [Paste Resume].

🎯 Prompt 6: Intelligent Question Formulator

Create 10 insightful questions to ask the interviewer about [Company Name] and [Job Title]. Explain the strategic purpose behind each question. Suggest follow-up talking points based on potential answers. Company recent news: [Company News]

🎯 Prompt 7: Mock Interview Simulator

Design a 20-minute mock interview script for [Job Title]. Include a mix of common, behavioral, and technical questions. Provide ideal answer structures and evaluation criteria for each question. My technical skills: [Technical Skills]

🎯 Prompt 8: Thank-You Email Template

Write a post-interview thank-you email template for [Job Title] at [Company Name]. Include personalization points and reinforce key qualifications. Suggest 3 variations: standard, following-up, and second-round interview. Keep under 200 words. My interview highlights: [Interview Highlights].

Understanding Zero-Shot Learning in Natural Language Processing(NLP)

Understanding Zero-Shot Learning in NLP

Zero-shot learning (ZSL) is a fascinating technology in natural language processing (NLP) that allows models to handle tasks they haven’t been specifically trained for. This is incredibly useful when there’s not enough labeled data available. Let’s explore some practical examples of how ZSL is used in NLP.

Text Classification

Imagine you have a model trained to classify news articles into categories like politics and sports. With ZSL, this model can also classify articles into new categories like technology or health without needing additional training. It does this by using descriptions of these new categories to understand what they are about.

Sentiment Analysis

ZSL is great for sentiment analysis across different languages. For example, a model trained to understand English reviews can also analyze reviews in Spanish or French without needing labeled data in those languages. This is perfect for companies that want to understand customer feedback from around the world.

Named Entity Recognition (NER)

In named entity recognition, ZSL helps identify new types of entities in text. For instance, a legal document might mention specific laws or regulations that weren’t part of the training data. A ZSL model can still recognize these new entities by using context clues and descriptions.

Machine Translation

ZSL can also improve machine translation. Suppose a model is trained to translate between English and Spanish. With ZSL, it can also translate between English and Italian, even if it hasn’t seen Italian before. This makes translation services more versatile and accessible.

Question Answering

In question-answering systems, ZSL allows models to answer questions about topics they haven’t been trained on. For example, a customer service bot can handle new types of queries by understanding the context and generating relevant answers.

Content Moderation

Social media platforms use ZSL for content moderation. A ZSL model can identify and flag harmful or inappropriate content that wasn’t part of its training data. This helps keep online communities safe and respectful.

Conclusion

Zero-shot learning makes NLP models more flexible and powerful. By allowing models to generalize from known to unknown categories, ZSL is transforming text classification, sentiment analysis, named entity recognition, machine translation, question answering, and content moderation. As ZSL technology advances, it will continue to make our interactions with technology smoother and more intuitive.

Building a Scalable Distributed Log Analytics System: A Comprehensive Guide

 Designing a distributed log analytics system involves several key components and considerations to ensure it can handle large volumes of log data efficiently and reliably. Here’s a high-level overview of the design:

1. Requirements Gathering

• Functional Requirements:
  • Log Collection: Collect logs from various sources.
  • Log Storage: Store logs in a distributed and scalable manner.
  • Log Processing: Process logs for real-time analytics.
  • Querying and Visualization: Provide tools for querying and visualizing log data.
• Non-Functional Requirements:
  • Scalability: Handle increasing volumes of log data.
  • Reliability: Ensure data is not lost and system is fault-tolerant.
  • Performance: Low latency for log ingestion and querying.
  • Security: Secure log data and access.

2. Architecture Components

• Log Producers: Applications, services, and systems generating logs.
• Log Collectors: Agents or services that collect logs from producers (e.g., Fluentd, Logstash).
• Message Queue: A distributed queue to buffer logs (e.g., Apache Kafka).
• Log Storage: A scalable storage solution for logs (e.g., Elasticsearch, Amazon S3).
• Log Processors: Services to process and analyze logs (e.g., Apache Flink, Spark).
• Query and Visualization Tools: Tools for querying and visualizing logs (e.g., Kibana, Grafana).

3. Detailed Design

• Log Collection:
  • Deploy log collectors on each server to gather logs.
  • Use a standardized log format (e.g., JSON) for consistency.
• Message Queue:
  • Use a distributed message queue like Kafka to handle high throughput and provide durability.
  • Partition logs by source or type to balance load.
• Log Storage:
  • Store logs in a distributed database like Elasticsearch for fast querying.
  • Use object storage like Amazon S3 for long-term storage and archival.
• Log Processing:
  • Use stream processing frameworks like Apache Flink or Spark Streaming to process logs in real-time.
  • Implement ETL (Extract, Transform, Load) pipelines to clean and enrich log data.
• Query and Visualization:
  • Use tools like Kibana or Grafana to create dashboards and visualizations.
  • Provide a query interface for ad-hoc log searches.

4. Scalability and Fault Tolerance

• Horizontal Scaling: Scale out log collectors, message queues, and storage nodes as needed.
• Replication: Replicate data across multiple nodes to ensure availability.
• Load Balancing: Distribute incoming log data evenly across collectors and storage nodes.
• Backup and Recovery: Implement backup strategies for log data and ensure quick recovery in case of failures.

5. Monitoring and Maintenance

• Monitoring: Use monitoring tools to track system performance, log ingestion rates, and query latencies.
• Alerting: Set up alerts for system failures, high latencies, or data loss.
• Maintenance: Regularly update and maintain the system components to ensure optimal performance.

Example Technologies

• Log Collectors: Fluentd, Logstash.
• Message Queue: Apache Kafka.
• Log Storage: Elasticsearch, Amazon S3.
• Log Processors: Apache Flink, Spark.
• Query and Visualization: Kibana, Grafana.

Back-of-the-envelope calculations for designing a distributed log analytics system

Assumptions

1. Log Volume: Assume each server generates 1 GB of logs per day.
2. Number of Servers: Assume we have 10,000 servers.
3. Retention Period: Logs are retained for 30 days.
4. Log Entry Size: Assume each log entry is 1 KB.
5. Replication Factor: Assume a replication factor of 3 for fault tolerance.

Calculations

1. Daily Log Volume

• Total Daily Log Volume:

  $$1\text{ GB/server/day}\times 10,000\text{ servers}=10,000\text{ GB/day}=10\text{ TB/day}$$

2. Total Log Volume for Retention Period

• Total Log Volume for 30 Days:

  $$10\text{ TB/day}\times 30\text{ days}=300\text{ TB}$$

3. Storage Requirement with Replication

• Total Storage with Replication:

  $$300\text{ TB}\times 3=900\text{ TB}$$

4. Log Entries per Day

• Log Entries per Day:

  1 KB/entry10,000 GB/day×1,024 MB/GB×1,024 KB/MB​=10,485,760,000 entries/day

5. Log Entries per Second

• Log Entries per Second:

  24×60×60 seconds10,485,760,000 entries/day​≈121,215 entries/second

Summary

• Daily Log Volume: 10 TB.
• Total Log Volume for 30 Days: 300 TB.
• Total Storage with Replication: 900 TB.
• Log Entries per Second: Approximately 121,215 entries/second

Designing a Scalable Distributed Cache System for 1 Billion Queries Per Minute

 

Designing a distributed cache system to handle 1 billion queries per minute for both read and write operations is a complex task. Here’s a high-level overview of how you might approach this:

1. Requirements Gathering

• Functional Requirements:
  • Read Data: Quickly retrieve data from the cache.
  • Write Data: Store data in the cache.
  • Eviction Policy: Automatically evict least recently/frequently used items.
  • Replication: Replicate data across multiple nodes for fault tolerance.
  • Consistency: Ensure data consistency across nodes.
  • Node Management: Add and remove cache nodes dynamically.
• Non-Functional Requirements:
  • Performance: Low latency for read and write operations.
  • Scalability: System should scale horizontally by adding more nodes.
  • Reliability: Ensure high availability and fault tolerance.
  • Durability: Persist data if required.
  • Security: Secure access to the cache system.

2. Capacity Estimation

• Traffic Estimate:
  • Read Traffic: Estimate the number of read requests per second.
  • Write Traffic: Estimate the number of write requests per second.
• Storage Estimate:
  • Data Size: Estimate the average size of each cache entry.
  • Total Data: Calculate the total amount of data to be stored in the cache.

3. High-Level Design

• Architecture:
  • Client Layer: Handles requests from users.
  • Load Balancer: Distributes incoming requests across cache nodes.
  • Cache Nodes: Multiple servers storing cached data.
  • Database: Persistent storage for data.
• Data Partitioning:
  • Use consistent hashing to distribute data across cache nodes.
• Replication:
  • Replicate data across multiple nodes to ensure fault tolerance.
• Eviction Policy:
  • Implement LRU (Least Recently Used) or LFU (Least Frequently Used) eviction policies.

4. Detailed Design

• Cache Write Strategy:
  • Write-Through: Data is written to the cache and the database simultaneously.
  • Write-Back: Data is written to the cache first and then to the database asynchronously.
• Cache Read Strategy:
  • Read-Through: Data is read from the cache, and if not found, it is fetched from the database and then cached.
• Consistency Models:
  • Strong Consistency: Ensures that all nodes have the same data at any given time.
  • Eventual Consistency: Ensures that all nodes will eventually have the same data, but not necessarily immediately.

5. Scalability and Fault Tolerance

• Horizontal Scaling: Add more cache nodes to handle increased load.
• Auto-Scaling: Automatically add or remove nodes based on traffic.
• Fault Tolerance: Use replication and data sharding to ensure data availability even if some nodes fail.

6. Monitoring and Maintenance

• Monitoring: Use tools to monitor cache performance, hit/miss ratios, and node health.
• Maintenance: Regularly update and maintain the cache system to ensure optimal performance.

Example Technologies

• Cache Solutions: Redis, Memcached.
• Load Balancers: NGINX, HAProxy.
• Monitoring Tools: Prometheus, Grafana.

This is a high-level overview, and each component can be further detailed based on specific requirements and constraints¹²³. 

Back of Envelops Calculations:

1. Traffic Estimate

• Total Queries: 1 billion queries per minute.
• Queries per Second (QPS):

  1,000,000,000 queries / 60 seconds ​= 16,666,667 QPS

• Read/Write Ratio: Assume 80% reads and 20% writes.
  • Read QPS:

    $$16,666,667\times 0.8=13,333,334\text{ reads per second}$$

  • Write QPS:

    $$16,666,667\times 0.2=3,333,333\text{ writes per second}$$

2. Storage Estimate

• Average Size of Each Cache Entry: Assume each entry is 1 KB.
• Total Data Stored: Assume the cache should store data for 1 hour.
  • Total Entries per Hour:

    $$16,666,667\text{ QPS }\times 3600\text{ seconds }=60,000,000,000\text{ entries}$$

  • Total Data Size:

    $$60,000,000,000\text{ entries}\times 1\text{ KB }=60,000,000,000\text{ KB }=60\text{ TB}$$

3. Node Estimation

• Cache Node Capacity: Assume each cache node can handle 100,000 QPS and store 1 TB of data.
• Number of Nodes for QPS:

  16,666,667 QPS ​/ 100,000 QPS/node = 167 nodes

• Number of Nodes for Storage:

  60 TB​ / 1 TB/node = 60 nodes

• Total Number of Nodes:

  $$\max (167,60)=167\text{ nodes}$$

4. Replication Factor

• Replication Factor: Assume a replication factor of 3 for fault tolerance.
• Total Nodes with Replication:

  $$167\text{ nodes }\times 3=501\text{ nodes}$$

Summary

• Total Queries per Second: 16,666,667 QPS.
• Read QPS: 13,333,334 reads per second.
• Write QPS: 3,333,333 writes per second.
• Total Data Stored: 60 TB.
• Total Cache Nodes Required: 501 nodes (with replication).

To estimate the RAM required for the distributed cache system, we need to consider the following factors:

1. Data Storage: The amount of data stored in the cache.
2. Overhead: Additional memory required for metadata, indexing, and other overheads.

Data Storage

From our previous calculation:

• Total Data Stored: 60 TB (60,000,000,000 KB).

Overhead

Assume an overhead of 10% for metadata and indexing.

Total Memory Requirement

• Total Memory for Data: 60 TB.
• Total Overhead:

  $$60\text{ TB}\times 0.10=6\text{ TB}$$

• Total RAM Required:

  $$60\text{ TB}+6\text{ TB}=66\text{ TB}$$

Per Node Memory Requirement

Assuming we have 501 nodes (with replication):

• RAM per Node:

  66 TB​ / 501 nodes ≈ 132 GB/node

Summary

• Total RAM Required: 66 TB.
• RAM per Node: Approximately 132 GB.

This is a simplified example, and actual capacity planning would need to consider additional factors like network latency, data consistency, and failover strategies. 

WebSockets vs Server-Sent Events (SSE): Understanding Real-Time Communication Technologies

 WebSockets and Server-Sent Events (SSE) are both technologies used for real-time communication between a server and a client, but they have some key differences:

WebSockets

• Full Duplex Communication: WebSockets provide full-duplex communication, meaning data can be sent and received simultaneously between the client and server.
• Two-Way Communication: Both the client and server can initiate messages. This makes WebSockets suitable for applications where real-time updates are needed from both sides, such as chat applications, online gaming, or collaborative tools.
• Protocol: WebSockets establish a single long-lived connection over TCP. They start as an HTTP handshake, then switch to the WebSocket protocol.
• Binary and Text Data: WebSockets can send binary and text data, making them versatile for various applications.
• Use Cases: Ideal for real-time applications where both the client and server need to send messages independently, such as chat applications, live gaming, financial tickers, and collaborative editing tools.

Server-Sent Events (SSE)

• Unidirectional Communication: SSE allows the server to push updates to the client, but the client cannot send messages to the server over the same connection. The client can only receive messages.
• One-Way Communication: The communication is one-way from the server to the client. A separate HTTP request must be made if the client needs to send data to the server.
• Protocol: SSE uses HTTP and keeps the connection open for continuous updates. It's simpler as it doesn't require a protocol switch like WebSockets.
• Text Data Only: SSE can only send text data. If binary data is needed, it must be encoded as text (e.g., Base64).
• Automatic Reconnection: SSE includes built-in support for automatic reconnection if the connection is lost, which simplifies handling connection stability.
• Use Cases: Suitable for applications where the server needs to push updates to the client regularly, such as news feeds, live sports scores, or stock price updates.

Comparison Table

Feature	WebSockets	Server-Sent Events (SSE)
Communication Type	Full duplex (two-way)	Unidirectional (one-way)
Initiation of Messages	Both client and server	Server only
Protocol	Starts as HTTP, then switches to WebSocket	HTTP
Data Types	Binary and text	Text only
Complexity	More complex, requires protocol switch	Simpler, remains HTTP
Automatic Reconnection	Requires manual handling	Built-in
Use Cases	Chat apps, live gaming, financial tickers, collaborative tools	News feeds, live scores, stock price updates

Conclusion

• WebSockets are best suited for applications requiring bidirectional communication and real-time interactivity.
• SSE is more suitable for applications where the server needs to push continuous updates to the client with a simpler setup.

How to Design a Real-Time Stock Market and Trading App: A Comprehensive Guide

 Designing a real-time system for a stock market and trading application involves several critical components to ensure low latency, high availability, and security. Here's a structured approach to designing such a system:

1. Requirements Gathering

• Functional Requirements:

  • Real-time stock price updates
  • Trade execution
  • Portfolio management
  • User authentication and authorization
  • Historical data access
  • Notification and alert system

• Non-functional Requirements:

  • Low latency
  • High availability and scalability
  • Data security
  • Fault tolerance
  • Compliance with regulatory requirements

2. System Architecture

• Frontend:

  • Web and Mobile Apps: Use frameworks like React for web and React Native for mobile to ensure a responsive and dynamic user interface.
  • Real-time Data Display: WebSockets or Server-Sent Events (SSE) for real-time updates.

• Backend:

  • API Gateway: Central point for managing API requests. Tools like Kong or Amazon API Gateway.
  • Microservices Architecture: Different services for user management, trading, market data, portfolio management, etc.
  • Data Processing:
    • Message Brokers: Kafka or RabbitMQ for handling real-time data streams.
    • Stream Processing: Apache Flink or Spark Streaming for processing and analyzing data in real-time.
  • Database:
    • Time-Series Database: InfluxDB or TimescaleDB for storing historical stock prices.
    • Relational Database: PostgreSQL or MySQL for transactional data.
    • NoSQL Database: MongoDB or Cassandra for user sessions and caching.

• Market Data Integration:

  • Connect to stock exchanges and financial data providers via APIs for real-time market data.

3. Key Components

• Real-time Data Feed:

  • Data Ingestion: Use APIs from stock exchanges or financial data providers.
  • Data Processing: Stream processing to clean and transform the data.
  • Data Distribution: WebSockets or SSE to push data to clients.

• Trade Execution Engine:

  • Order Matching: Matching buy and sell orders with minimal latency.
  • Risk Management: Implementing checks to manage trading risks.
  • Order Routing: Directing orders to appropriate exchanges or internal pools.

• User Management:

  • Authentication and Authorization: Use OAuth or JWT for secure user authentication.
  • User Profiles: Manage user data and preferences.

• Portfolio Management:

  • Real-time Portfolio Updates: Track and update portfolio value based on market changes.
  • Historical Data: Provide access to historical trades and performance metrics.

• Notifications and Alerts:

  • Push Notifications: Notify users of critical events or changes.
  • Email/SMS Alerts: Send alerts for important updates or threshold breaches.

4. Infrastructure

• Cloud Providers: AWS, Azure, or Google Cloud for scalable infrastructure.
• Load Balancers: Distribute traffic across multiple servers to ensure high availability.
• CDN: Content Delivery Network to reduce latency for global users.
• Monitoring and Logging: Tools like Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana) for monitoring and logging.

5. Security and Compliance

• Encryption: Encrypt data in transit (TLS/SSL) and at rest (AES).
• DDoS Protection: Implement DDoS protection to guard against attacks.
• Regulatory Compliance: Ensure compliance with regulations like GDPR, MiFID II, etc.

6. Testing and Deployment

• CI/CD Pipeline: Continuous Integration and Continuous Deployment for frequent and reliable releases.
• Testing: Automated testing (unit, integration, and end-to-end) to ensure system reliability.
• Canary Releases: Gradually roll out updates to a small user base before full deployment.

7. Performance Optimization

• Caching: Use Redis or Memcached to cache frequent queries.
• Database Optimization: Indexing, query optimization, and database sharding.
• Network Optimization: Use efficient protocols and minimize data transfer.

8. User Experience

• Intuitive Interface: Design a user-friendly interface with clear navigation.
• Responsive Design: Ensure the app works well on various devices and screen sizes.
• Accessibility: Make the app accessible to users with disabilities.

By integrating these components and considerations, you can design a robust and efficient real-time stock market and trading application that meets user expectations and industry standards.

How Do You Calculate Network Bandwidth Requirements Based on Estimated Traffic Volume and Data Transfer Sizes?

To calculate the required network bandwidth, you need to consider the estimated traffic volume and data transfer sizes. Here's a step-by-step guide to help you estimate the bandwidth requirements:

1. Identify the Traffic Volume

Determine the number of users or devices that will be using the network and how frequently they will be sending or receiving data.

2. Determine Data Transfer Sizes

Estimate the size of the data each user or device will transfer during each session or over a specific period (e.g., per second, minute, or hour).

3. Calculate Total Data Transfer

Multiply the number of users or devices by the data transfer size to get the total data transferred over the period.

Total Data Transfer=Number of Users/Devices×Data Transfer Size

4. Convert Data Transfer to Bits

Convert the data transfer size from bytes to bits (since bandwidth is usually measured in bits per second).

Bits=Bytes×8

5. Determine the Period

Decide the time period over which the data is transferred (e.g., per second, per minute, etc.).

6. Calculate Bandwidth

Finally, divide the total bits transferred by the time period to get the bandwidth in bits per second (bps).

Adjust these calculations based on your specific traffic volume and data transfer sizes to estimate your network bandwidth requirements accurately.