If you tell me your exact distro (e.g., Ubuntu 22.04, Mint 21, Lubuntu, etc.), I’ll give you the exact filename and best command.
...
(base) jain@jain-ThinkPad-T440s:~/Downloads$ chmod +x drawio-x86_64-29.0.3.AppImage
(base) jain@jain-ThinkPad-T440s:~/Downloads$ ./drawio-x86_64-29.0.3.AppImage
dlopen(): error loading libfuse.so.2
AppImages require FUSE to run.
You might still be able to extract the contents of this AppImage
if you run it with the --appimage-extract option.
See https://github.com/AppImage/AppImageKit/wiki/FUSE
for more information
This is a very common AppImage issue 👍
Your system is missing FUSE v2 (libfuse.so.2), which AppImages require.
Let’s fix it cleanly.
✅ Fix 1 (Recommended): Install FUSE (libfuse2)
On Ubuntu / Linux Mint / Lubuntu:
bash
sudo apt update
sudo apt install libfuse2
Then run again:
bash
./drawio-x86_64-29.0.3.AppImage
This should start draw.io immediately.
🔎 Why this happens
AppImages use FUSE (Filesystem in Userspace) to mount themselves.
Newer distros ship fuse3, but AppImages still expect fuse2.
Hence the missing libfuse.so.2.
🛠 Fix 2 (If you cannot install FUSE): Extract AppImage
If you’re on Ubuntu/Mint/Lubuntu, the DEB version is simpler:
bash
sudo apt install ./drawio-amd64-29.0.3.deb
✅ Quick Recommendation
Situation
Best Choice
Ubuntu / Mint / Lubuntu
DEB package
Portable / no install
AppImage + libfuse2
Locked-down system
AppImage extract
If you want, I can:
Create a desktop launcher for the AppImage
Help you switch to the DEB version
Check if your system already has fuse3 vs fuse2
Just tell me 👍
... ... ...
(base) jain@jain-ThinkPad-T440s:~/Downloads$ ./drawio-x86_64-29.0.3.AppImage
[6616:1227/063742.336623:FATAL:sandbox/linux/suid/client/setuid_sandbox_host.cc:166] The SUID sandbox helper binary was found, but is not configured correctly. Rather than run without sandboxing I'm aborting now. You need to make sure that /tmp/.mount_drawiodOPId7/chrome-sandbox is owned by root and has mode 4755.
Trace/breakpoint trap (core dumped)
This is another very common Electron + AppImage issue 👍
Nothing is “broken” — it’s a sandbox permission problem.
The error means:
Electron found the chrome-sandbox helper inside the AppImage, but it cannot be setuid root, which is required for sandboxing.
Because AppImages run from /tmp/.mount_*, Linux does not allow setuid binaries there.
✅ Fastest & Safest Fix (Recommended)
Run draw.io without the sandbox.
👉 Just run:
bash
./drawio-x86_64-29.0.3.AppImage --no-sandbox
This is safe for desktop apps like draw.io and is the official workaround.
🔁 Make it permanent (so you don’t type it every time)
You are developing a large language model using GPT-3 and want to apply few-shot learning techniques. You have a limited dataset for a specific task and want the model to generalize well. Which approach would be most effective?
Options:
a) Train the model on the entire dataset, then fine-tune on a small subset
b) Provide examples of the task in the input and let the model generate
c) LLMs are unable to handle single tasks
✅ Correct Answer
✔ b) Provide examples of the task in the input and let the model generate
💡 Hint
Few-shot learning = prompt engineering
No retraining required
You show the task via examples in the prompt
Q2. Edge AI with LLMs
Question
You are using an LLM for an edge AI application requiring real-time object detection. Which approach is most efficient?
Options:
a) Cloud-based LLM processing
b) Use a complex LLM regardless of compute
c) Use an LLM optimized for edge devices balancing accuracy and efficiency
d) Store data and process later
e) Manual input-based LLM
You fine-tuned a pre-trained LLM but performance is poor. What steps improve it?
Options:
a) Gather more annotated Q&A data with better supervision
b) Change architecture to Mixture of Experts / Mixture of Tokens
c) Simplify the task definition
d) Smaller chunks reduce retrieval complexity
e) Smaller chunks improve generation accuracy
✅ Correct Answers
✔ a) Gather more annotated data
✔ c) Simplify the task definition
What is a potential challenge local LLMs face in long-term task planning?
Options:
a) Unable to adjust plans when errors occur
b) Unable to handle complex tasks
c) Unable to handle multiple queries
d) Unable to use task decomposition
✅ Correct Answer
✔ a) Unable to adjust plans when errors occur
💡 Hint
Local models lack persistent memory & feedback loops
Planning correction is the real limitation
Q6. RAG Pipeline – Poor Semantic Representation
Question
Why might embeddings not represent semantic meaning correctly?
Options:
a) Encoder not trained on similar data
b) Text chunks too large
c) Encoder incompatible with RAG
d) Incorrect chunk splitting
e) Encoder not initialized
✅ Correct Answers
✔ a) Domain mismatch in training data
✔ b) Chunk size too large
💡 Hint
Embeddings fail due to domain shift or context overflow
Initialization issues are rare in practice
Q7. Designing Advanced RAG – Chunking Decision
Question
Which is NOT a valid reason for splitting documents into smaller chunks?
Options:
a) Large chunks are harder to search
b) Small chunks won’t fit in context window
c) Smaller chunks improve indexing efficiency
✅ Correct Answer
✔ b) Small chunks won’t fit in context window
💡 Hint
Smaller chunks fit better, not worse
This is a classic reverse-logic trap
Q8. Intent in Chatbots
Question
What is the purpose of an intent in a chatbot?
✅ Correct Answer
✔ To determine the user’s goal
💡 Hint
Intent ≠ entity
Intent answers “what does the user want?”
Q9. Healthcare LLM Security
Question
Which strategy best ensures privacy and compliance for patient data?
Options:
a) Public API & public cloud
b) Layered security: encryption, access control, audits, private network
c) No security changes
d) Plaintext storage
e) Unverified 3rd party services
✅ Correct Answer
✔ b) Layered security approach
💡 Hint
Healthcare = defense in depth
Accenture loves encryption + audits + private infra
Q10. Edge AI Programming Language
Question
Which language is commonly used for developing ML models in Edge AI?
Options:
Java
Python
C++
JavaScript
Ruby
✅ Correct Answer
✔ Python
💡 Hint
Python dominates ML tooling
C++ is used for deployment, not modeling
Q11. Customizing METEOR Scoring
Question
How do you customize METEOR’s scoring function?
✅ Correct Answer
✔ Modify tool configuration or run with command-line flags
💡 Hint
METEOR supports custom weighting
Not hardcoded, no paid version needed
Q12. Bias Mitigation in LLMs
Question
First step when an LLM is found biased?
✅ Correct Answer
✔ Identify the source of bias
💡 Hint
Diagnosis before correction
Retraining comes later
Q13. DeepEval Tool – Advanced Features
Question
Which statement is correct about DeepEval advanced usage?
✅ Correct Answer
✔ Configure advanced features in Python scripts
💡 Hint
Tool-level configuration
No paid version needed
Tags: Interview Preparation,Generative AI,Large Language Models,
Q. How do you stay updated with the latest trends in AI and machine learning?
A.
# Staying updated with the latest trends in AI and machine learning is crucial for my professional development. I regularly engage with online courses and certifications, such as those from DeepLearning.AI and Google Cloud Skills Boost, to enhance my knowledge.
# Additionally, I follow industry leaders and research publications to keep abreast of new developments. Participating in webinars and conferences also provides valuable insights into emerging technologies and methodologies.
# Networking with other professionals in the field allows me to exchange ideas and learn from their experiences. This collaborative approach helps me stay informed about best practices and innovative solutions in AI.
# By actively pursuing continuous learning, I ensure that I remain at the forefront of advancements in AI and machine learning, which ultimately benefits my projects and teams.
Q: What strategies do you use for fine-tuning large language models for specific applications?
A:
Fine-tuning large language models (LLMs) is a critical aspect of my work. I typically start by understanding the specific requirements of the application, such as the domain and the type of data involved. For instance, in my project 'AI Over BI', we fine-tuned models to handle domain-specific queries effectively.
My approach involves collecting a diverse dataset that reflects the types of queries users might pose. I then preprocess this data to ensure it is clean and relevant. Using frameworks like LangChain and HuggingFace Transformers, I implement fine-tuning techniques that adjust the model's weights based on this dataset.
Moreover, I continuously evaluate the model's performance through metrics such as accuracy and response time, making iterative adjustments as necessary. This process ensures that the model not only understands the context but also provides accurate and timely responses.
Q. Can you discuss your experience with cloud technologies in deploying AI solutions?
A:
My experience with cloud technologies is extensive, particularly in deploying AI solutions. I have worked with platforms such as Azure Cloud and Google Collaboratory to implement scalable AI applications.
In my current role at Accenture, I have utilized Azure ML Studio to deploy models developed for various projects, including the 'AI Over BI' initiative. This platform allows for seamless integration of machine learning workflows and provides tools for monitoring and managing deployed models.
Additionally, I have experience with MLOps practices, which are essential for maintaining the lifecycle of AI models. This includes version control using Git and implementing CI/CD pipelines to automate the deployment process.
By leveraging cloud technologies, I ensure that the AI solutions I develop are not only efficient but also scalable, allowing them to handle large datasets and user requests effectively.
Q. How do you approach prompt engineering for LLMs in your projects?
A:
Prompt engineering is a crucial skill in my toolkit, especially when working with large language models. In my role at Accenture, I have developed a systematic approach to crafting effective prompts that yield the best results from LLMs.
Initially, I focus on understanding the specific task at hand and the expected output. For example, in the 'AI Over BI' project, I needed to ensure that the prompts accurately reflected the user's intent when querying the database.
I employ techniques such as meta-prompting, where I refine the initial prompt based on feedback from the model's responses. This iterative process allows me to adjust the wording and structure of the prompts to improve clarity and relevance.
Additionally, I leverage my knowledge of embeddings and attention mechanisms to enhance the prompts further. By incorporating context and ensuring that the prompts are concise yet informative, I can significantly improve the model's performance in generating accurate and relevant outputs.
Q. Can you describe your experience with Generative AI and how you've applied it in projects?
A:
In my current role as a Senior Data Scientist at Accenture Solutions Pvt Ltd, I have been deeply involved in projects leveraging Generative AI. One notable project is the 'AI Over BI' initiative, where we developed a solution that utilizes Generative AI to enhance business intelligence across various domains, including Telco and Healthcare.
This project involved preprocessing data to generate metadata about tables and columns, which was then stored in a vector database. We implemented a Natural Language Query (NLQ) system that allowed users to interact with the data using natural language, which was re-engineered into SQL queries for execution. This not only streamlined data access but also improved user engagement with the analytics platform.
Additionally, I worked on an English Language Learning App as part of Accenture's CSR initiative, which utilized Generative AI for features like sentence generation and story creation. This project aimed to enhance learning experiences for students in low-income schools, showcasing the versatility of Generative AI in educational contexts.
Q. How do you ensure that your AI solutions are scalable and maintainable?
Ensuring scalability and maintainability in AI solutions is a priority in my work. I adopt a modular approach when designing AI systems, which allows for easier updates and scalability.
For example, in my projects at Accenture, I utilize cloud-native architectures that support scaling based on user demand. This includes leveraging services from Azure Cloud to handle increased workloads without compromising performance.
Additionally, I implement best practices in coding and documentation, which are essential for maintainability. Clear documentation helps other team members understand the system, making it easier to onboard new developers and maintain the codebase.
Regular code reviews and testing are also part of my strategy to ensure that the solutions remain robust and scalable. By continuously evaluating the architecture and performance, I can make necessary adjustments to accommodate growth.
Q. Can you explain your experience with Retrieval-Augmented Generation (RAG) architectures?
A:
My experience with Retrieval-Augmented Generation (RAG) architectures is extensive, particularly in my current role at Accenture. In the 'AI Over BI' project, we developed a RAG pipeline that integrated structured and unstructured data to enhance user interactions with our analytics platform.
The RAG architecture allowed us to retrieve relevant information from a vector database based on user queries. This involved preprocessing data to create metadata and using it to inform the model about the context of the queries. By re-engineering user prompts through meta-prompting techniques, we ensured that the LLM could generate accurate SQL queries.
This architecture not only improved the accuracy of the responses but also enhanced the overall user experience by providing contextually relevant information quickly. The success of this project demonstrated the power of RAG in bridging the gap between data retrieval and natural language generation.
Q. Can you describe a project where you had to mentor junior data scientists?
A:
Mentoring junior data scientists has been a rewarding aspect of my career, particularly during my time at Accenture. One notable project involved the development of the 'Wiki Spinner' knowledge automation platform.
In this project, I took on the responsibility of guiding a team of junior data scientists through the complexities of implementing Generative AI solutions. I provided them with insights into best practices for data preprocessing, model selection, and evaluation techniques.
We held regular meetings to discuss progress and challenges, fostering an environment of open communication. I encouraged them to share their ideas and approaches, which not only boosted their confidence but also led to innovative solutions.
By the end of the project, the junior team members had significantly improved their skills and contributed meaningfully to the project's success. This experience reinforced my belief in the importance of mentorship in fostering talent within the field of data science.
Q. What role do you believe team collaboration plays in successful AI projects?
A:
Team collaboration is vital in the success of AI projects. In my experience, particularly at Accenture, I have seen firsthand how effective collaboration can lead to innovative solutions and improved project outcomes.
Working in cross-functional teams allows for diverse perspectives, which is crucial when developing complex AI solutions. For instance, in the 'AI Over BI' project, collaboration with product managers and engineers helped us align our AI capabilities with business needs.
I also believe in fostering an environment where team members feel comfortable sharing ideas and feedback. This open communication leads to better problem-solving and enhances the overall quality of the project.
Moreover, mentoring junior data scientists is another aspect of collaboration that I value. By sharing knowledge and best practices, I contribute to the team's growth and ensure that we collectively advance our skills in AI and machine learning.
Q. What are your long-term goals in the field of data science?
A:
My long-term goals in the field of data science revolve around advancing my expertise in Generative AI and its applications across various industries. I aspire to lead innovative projects that leverage AI to solve complex problems and drive business value.
Additionally, I aim to contribute to the development of best practices in AI ethics and governance, ensuring that the solutions we create are responsible and beneficial to society.
Mentoring the next generation of data scientists is also a key goal for me. I believe that sharing knowledge and experiences can help cultivate a strong community of professionals who are equipped to tackle the challenges of the future.
Ultimately, I envision myself in a leadership role where I can influence the strategic direction of AI initiatives within an organization, driving innovation and fostering a culture of continuous learning and improvement.
Q. What is your experience with model evaluation and optimization techniques?
A:
Model evaluation and optimization are integral parts of my workflow as a Senior Data Scientist. I utilize various metrics to assess model performance, including accuracy, precision, recall, and F1 score, depending on the specific application.
For instance, in my recent projects at Accenture, I implemented a rigorous evaluation framework for the models used in the 'AI Over BI' initiative. This involved testing the models against a validation dataset to ensure they could handle real-world queries effectively.
Once the models were evaluated, I focused on optimization techniques such as hyperparameter tuning and feature selection. By adjusting parameters and selecting the most relevant features, I was able to enhance the model's performance significantly.
Moreover, I continuously monitor the models post-deployment to ensure they maintain their performance over time. This proactive approach allows me to identify any degradation in model accuracy and make necessary adjustments promptly.
