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PDPA - 3

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20 questions
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  • Q1
    1. In a high-volume automation scenario involving multiple no-code/low-code platforms, which advanced architectural practice ensures fault tolerance and scalability for Python-based microservices?
    Deploying a single monolithic script on a shared network drive
    Hardcoding API endpoints into each script and relying on manual restarts when failures occur
    Using a message-driven microservice architecture (e.g., with queues or event buses) allowing horizontal scaling and graceful recovery
    Implementing only local file-based triggers for all integrations, without any distributed mechanism
    60s
  • Q2
    2. When building an enterprise-scale Streamlit application for process automation, which advanced method secures both data and user authentication across multiple domains?
    Relying solely on HTTP Basic Auth in plain text across internal networks
    Using only local user accounts on the host machine where Streamlit runs, without encryption
    Integrating a Single Sign-On (SSO) solution with secure tokens (e.g., OAuth or SAML), plus HTTPS/TLS and role-based access
    Embedding all credentials in the Streamlit config file and distributing that file to users
    60s
  • Q3
    3. In designing complex Python scripts that automate workflows across various no-code/low-code platforms, which advanced testing strategy is most effective for preventing regression issues?
    Implementing a suite of end-to-end tests with mocking/stubbing for external APIs and no-code platform services
    Only running manual spot checks in the production environment
    Restricting tests to basic unit tests that do not interact with any external services
    Relying on user acceptance tests in a single test environment without version control
    60s
  • Q4
    4. When integrating OAuth for external APIs in a mission-critical environment, which advanced security measure helps mitigate the risk of token interception or misuse?
    Storing and rotating tokens in a secure vault or via environment variables, along with short-lived tokens and refresh workflows
    Sharing tokens via email among development teams for quick troubleshooting
    Disabling token rotation to reduce overhead and avoid complexity
    Hardcoding tokens into the source code repository for easier access
    60s
  • Q5
    5. What is a key advanced challenge when using Python to parse large JSON/XML payloads from no-code/low-code integrations, and how should it be addressed?
    Network bottlenecks, addressed by storing all data in local CSV files before any parsing
    Strict data type mismatches, addressed by converting all data to text strings in one pass
    API rate limits, addressed by ignoring server 429 responses and retrying immediately
    Memory constraints, addressed by using streaming parsers or generators (e.g., ijson, lxml iterparse) rather than loading the entire payload at once
    60s
  • Q6
    6. In advanced distributed systems, how can a developer ensure transactional consistency when updating multiple services through APIs connected to no-code/low-code platforms?
    Updating the services in arbitrary order and ignoring any error handling unless critical data is lost
    Using a distributed transaction or saga pattern, ensuring each step has compensating actions if a subsequent step fails
    Relying solely on eventually consistent databases without tracking partial failures
    Making all updates simultaneously in parallel calls without any rollback strategy
    60s
  • Q7
    7. An advanced debugging scenario shows intermittent failures in a Python-based automation script triggered by a no-code workflow. Which approach is most suitable for isolating concurrency issues?
    Suppressing error logs to reduce noise and focusing solely on CPU usage
    Eliminating all asynchronous calls and forcing sequential execution for every process
    Adding a single print statement at the start of the script and hoping the logs suffice
    Deploying robust logging with timestamps and correlation IDs, plus employing concurrency stress testing (e.g., via load testing or chaos engineering)
    60s
  • Q8
    8. For high-throughput data exchange between Python scripts and no-code platforms, which advanced performance optimization technique often proves critical?
    Scaling vertically on a single server rather than distributing the load across multiple nodes
    Hardcoding time.sleep delays to manage traffic spikes
    Using asynchronous I/O or queuing mechanisms (e.g., Celery, RabbitMQ) to handle spikes in traffic without overloading resources
    Replacing all data exchange with manual CSV uploads once daily
    60s
  • Q9
    9. When constructing a multi-environment deployment strategy for Python scripts and no-code/low-code workflows, which advanced practice enables seamless rollbacks and upgrades?
    Storing old versions of code in an untracked local folder “just in case,” without formal versioning
    Utilizing containerization (e.g., Docker) and Infrastructure as Code (IaC) with versioned workflows, allowing quick rollbacks and parallel testing
    Using only manual instructions for deployment steps documented in a text file
    Deploying changes directly in production without staging or test environments
    60s
  • Q10
    10. In an enterprise setting, why might advanced logging and monitoring be necessary for Python-based automations that integrate multiple APIs through no-code platforms?
    To detect subtle performance bottlenecks, identify data synchronization issues, and provide real-time alerts for failure conditions
    To allow the system to run without logs so that it can execute faster
    To automatically reboot servers without investigating the root cause of issues
    To ensure full debugging information is printed directly to the console only
    60s
  • Q11
    11. In advanced usage of no-code/low-code solutions like Zoho Creator or Power Apps, how does a developer effectively handle edge cases that the platform’s built-in features do not cover?
    Re-writing the entire no-code/low-code platform in Python to bypass all constraints
    Declining requests for edge cases to maintain strict reliance on built-in features
    Extending core functionality with custom Python microservices, REST APIs, or external services that integrate seamlessly
    Forcing all requests through a single, generic script without additional logic
    60s
  • Q12
    12. Which advanced Python design pattern is especially helpful for orchestrating complex, conditional process flows spanning multiple no-code/low-code platforms?
    The Composite pattern used only for hierarchical data structures
    The State Machine or Workflow pattern, enabling clearly defined transitions and conditional branches
    The Proxy pattern primarily used for lazy loading database connections
    The Singleton pattern to ensure only one instance of the script runs at any time
    60s
  • Q13
    13. How does employing a micro-frontend approach with Python-based web apps (e.g., Streamlit) and no-code platforms benefit enterprise-scale automation projects?
    It eliminates the necessity of user authentication, as each micro-frontend is inherently secure
    It replaces the need for any centralized back-end or integration layer
    It allows separate, independently deployable UI components, each handling specific functionality, leading to quicker updates and reduced risk
    It forces all UI elements to be part of one monolithic front-end, ensuring that users see a single large interface
    60s
  • Q14
    14. When debugging an intricate automation pipeline involving Python scripts, no-code workflows, and external APIs, which advanced traceability technique is most effective?
    Relying on random log entries to piece together the flow post-mortem
    Using only local logs with no reference to external system interactions
    Storing debug data in ephemeral memory so it is lost after each run
    Propagating a unique correlation ID across all logs, requests, and error traces, allowing end-to-end visibility in distributed logs
    60s
  • Q15
    15. In an advanced enterprise setting, how should a developer address race conditions in Python scripts triggered concurrently by no-code workflows?
    Rely on the no-code platform’s default scheduling to avoid concurrency entirely
    Implement concurrency controls (e.g., locks, semaphores) or use transactionally safe data structures; design idempotent operations
    Assume that all simultaneous triggers run sequentially on the server by default
    Only handle concurrency by retrying failed operations indefinitely
    60s

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