You are extracting customer name data from multiple sources. Some sources return "John Smith"
while others return "smith, john". After combining data, you notice duplicate records that differ
only in name format. Which approach best handles this situation?
6406536004834.
Keep both formats since they contain same information
6406536004835.
Normalize to first/last name components, standardize case/order, then
deduplicate
6406536004836.
Delete all records appearing as duplicates arbitrarily
6406536004837.
Require users to manually resolve each formatting issue
A published solution is not available for this question yet.
Your team uses an external weather API in automated tests. The API sometimes returns
temporary errors (500 errors). Which testing approach is most reliable?
6406536004838.
Test against live API every time, accept occasional 500 failures
6406536004839.
Record real API responses as offline test files, use files normally, test live
periodically
6406536004840.
Skip API tests since provider handles reliability
6406536004841.
Mock the API by hardcoding expected weather values for specific cities
A published solution is not available for this question yet.
You are combining product data from two suppliers. Supplier A has product names like "Blue T-
Shirt Size M" and Supplier B has "tshirt blue m". Why is it difficult to identify these as the same
product using simple text matching?
6406536004842.
The names include extra descriptive words that do not appear in both supplier
listings.
6406536004843.
Effective text comparison requires accounting for variations in word order,
abbreviations, and synonyms, as a simple word-by-word match is insufficient.
6406536004844.
Text matching is impossible with product names
6406536004845.
The product codes are different so they must be different products
A published solution is not available for this question yet.
In a bash ETL script that runs on a schedule, what should you do to make sure the job can run
repeatedly without causing bad or duplicated results (i.e., to keep it idempotent)?
6406536004846.
Always write straight into the final output file and overwrite it every time
6406536004847.
Write to temp file, atomic move to final, track processed records
6406536004848.
Delete all output files at the start of every run
6406536004849.
Run the job several times at once so at least one run succeeds
A published solution is not available for this question yet.
You maintain a Python script that cleans data. The script uses an external library (e.g., pandas) that
your organization will support for the next 2 years. However, a new version of pandas is released
with bug fixes. What is the best practice?
6406536004854.
Immediately upgrade to the latest version of all dependencies
6406536004855.
Document current version, test new version separately, upgrade if compatible
6406536004856.
Never update any library since updates always break things
6406536004857.
Remove the pandas dependency and rewrite your script without it
A published solution is not available for this question yet.
Your team develops an automated pipeline that classifies customer support tickets into categories.
A team member used an AI assistant to write the classification code. The code works correctly in
testing but occasionally produces unexpected categories in production. What is the best way to
identify and fix this issue?
6406536004858.
Remove the AI-generated code and rewrite it from scratch
6406536004859.
Capture production failures as test cases, fix code, save tests permanently
6406536004860.
Accept occasional errors as normal and monitor the error rate
6406536004861.
Disable the AI assistant for the entire team
A published solution is not available for this question yet.
**Scenario 1: Store Location Analysis with Geospatial Data**
Based on the above data, answer the given subquestions.
You want to find all stores within 5km of the city center. Using GeoPandas, what is the correct
general approach?
6406536004878.
Manually calculate the difference between latitudes and compare to 5
6406536004879.
Create a Point for city center, create Points for stores, calculate distances
using the appropriate distance method, then filter stores where distance < 5000 meters
6406536004880.
Sort stores by latitude and pick the first 5
6406536004881.
Use pandas string matching on store names
A published solution is not available for this question yet.
**Scenario 1: Store Location Analysis with Geospatial Data**
Based on the above data, answer the given subquestions.
You have store coordinates in a CSV file. Which Python library combination is most appropriate for
performing geographic distance calculations and spatial analysis?
6406536004890.
Only pandas for reading CSV and calculating distances
6406536004891.
Pandas for data handling and GeoPandas for spatial operations (distance
calculations, buffers, spatial joins)
6406536004892.
Only matplotlib for creating maps
6406536004893.
Excel for all spatial calculations
A published solution is not available for this question yet.
**Scenario 2 : Weather API Integration for Event Planning**
**Context**
An event management company wants to automate weather data collection for planning outdoor
events. They need to fetch current weather and 5-day forecasts from a weather API for multiple
cities to help clients make informed decisions.
Based on the above data, answer the given subquestions.
To make a request to the weather API, which Python library is most commonly used for HTTP
requests?
6406536004894.
pandas
6406536004895.
requests (or httpx)
6406536004896.
flask
6406536004897.
beautifulsoup
A published solution is not available for this question yet.
**Scenario 2 : Weather API Integration for Event Planning**
**Context**
An event management company wants to automate weather data collection for planning outdoor
events. They need to fetch current weather and 5-day forecasts from a weather API for multiple
cities to help clients make informed decisions.
Based on the above data, answer the given subquestions.
The API requires an API key for authentication. Where should you store this API key in your code
to follow security best practices?
6406536004898.
Hard-code it directly in your Python script: api_key = "abc123xyz"
6406536004899.
Store it in an environment variable (e.g., os.getenv('WEATHER_API_KEY'))
6406536004900.
Write it in a comment in the code
6406536004901.
Include it in the GitHub repository README
A published solution is not available for this question yet.
**Scenario 2 : Weather API Integration for Event Planning**
**Context**
An event management company wants to automate weather data collection for planning outdoor
events. They need to fetch current weather and 5-day forecasts from a weather API for multiple
cities to help clients make informed decisions.
Based on the above data, answer the given subquestions.
After calling the API, you want to extract the weather description (e.g., “clear sky”) from the JSON
response. Which is the correct way to access it?
6406536004902.
response['weather']['description']
6406536004903.
response_json['weather'][0]['description']
6406536004904.
response['main']['weather']
6406536004905.
response_json['description']['weather']
A published solution is not available for this question yet.
**Scenario 2 : Weather API Integration for Event Planning**
**Context**
An event management company wants to automate weather data collection for planning outdoor
events. They need to fetch current weather and 5-day forecasts from a weather API for multiple
cities to help clients make informed decisions.
Based on the above data, answer the given subquestions.
You need to fetch weather for 10 cities, but the API has a rate limit of 60 calls per minute. How
should you structure your code?
6406536004906.
Make all 10 requests simultaneously as fast as possible
6406536004907.
Use a 1–2 sec sleep() between requests to avoid rate limits and handle errors.
6406536004908.
Ignore the rate limit since you're only making 10 calls
6406536004909.
Make requests only during off-peak hours
A published solution is not available for this question yet.
**Scenario 2 : Weather API Integration for Event Planning**
**Context**
An event management company wants to automate weather data collection for planning outdoor
events. They need to fetch current weather and 5-day forecasts from a weather API for multiple
cities to help clients make informed decisions.
Based on the above data, answer the given subquestions.
The API request fails with a 401 error status code. What does this indicate?
6406536004910.
The API server is down
6406536004911.
Unauthorized - likely an invalid or missing API key
6406536004912.
The city name is misspelled
6406536004913.
Too many requests (rate limit exceeded)
A published solution is not available for this question yet.
**Scenario 3: Sentiment Analysis with LLMs**
**Context**
A product team wants to analyze customer feedback from app reviews to understand user
sentiment and identify common issues. Instead of manual review, they use an LLM to
automatically classify sentiment and extract key themes from review text.
**Available Data:**
• 500 app reviews (text feedback, 1-5 star ratings)
• LLM API access (e.g., OpenAI GPT, Anthropic Claude)
• Goal: Classify sentiment (positive/negative/neutral) and extract main topics
Based on the above data, answer the given subquestions.
To send a review to an LLM API for sentiment analysis, which type of HTTP request would you
typically make?
6406536004914.
GET request to retrieve sentiment
6406536004915.
POST request with the review text in the request body
6406536004916.
DELETE request to remove the review
6406536004917.
PUT request to update the review
A published solution is not available for this question yet.
**Scenario 3: Sentiment Analysis with LLMs**
**Context**
A product team wants to analyze customer feedback from app reviews to understand user
sentiment and identify common issues. Instead of manual review, they use an LLM to
automatically classify sentiment and extract key themes from review text.
**Available Data:**
• 500 app reviews (text feedback, 1-5 star ratings)
• LLM API access (e.g., OpenAI GPT, Anthropic Claude)
• Goal: Classify sentiment (positive/negative/neutral) and extract main topics
Based on the above data, answer the given subquestions.
When designing a prompt for the LLM to classify sentiment, which prompt structure is most
effective?
6406536004918.
Just send the review text with no instructions
6406536004919.
Provide clear instructions and specify the desired output format: "Classify the
sentiment of this review as positive, negative, or neutral. Review: {review_text}"
6406536004920.
Send only the word "sentiment"
6406536004921.
Ask the LLM to write code instead
A published solution is not available for this question yet.
**Scenario 3: Sentiment Analysis with LLMs**
**Context**
A product team wants to analyze customer feedback from app reviews to understand user
sentiment and identify common issues. Instead of manual review, they use an LLM to
automatically classify sentiment and extract key themes from review text.
**Available Data:**
• 500 app reviews (text feedback, 1-5 star ratings)
• LLM API access (e.g., OpenAI GPT, Anthropic Claude)
• Goal: Classify sentiment (positive/negative/neutral) and extract main topics
Based on the above data, answer the given subquestions.
You're analyzing 500 reviews using an LLM API. Each API call costs $0.002 and takes 2 seconds.
What practical considerations should guide your approach?
6406536004922.
Send all 500 reviews in a single API call
6406536004923.
Process reviews in batches, track costs, handle errors, and cache results
6406536004924.
Only analyze 5 reviews to save money
6406536004925.
Call the API repeatedly for the same review
A published solution is not available for this question yet.
**Scenario 3: Sentiment Analysis with LLMs**
**Context**
A product team wants to analyze customer feedback from app reviews to understand user
sentiment and identify common issues. Instead of manual review, they use an LLM to
automatically classify sentiment and extract key themes from review text.
**Available Data:**
• 500 app reviews (text feedback, 1-5 star ratings)
• LLM API access (e.g., OpenAI GPT, Anthropic Claude)
• Goal: Classify sentiment (positive/negative/neutral) and extract main topics
Based on the above data, answer the given subquestions.
The LLM correctly identifies negative reviews but provides inconsistent output formats (sometimes
"negative", sometimes "Negative", sometimes "neg"). How do you handle this?
6406536004926.
Manually review and edit each LLM response to ensure consistent formatting
before using it in analysis
6406536004927.
Use prompt engineering to enforce exact output format (e.g., “Respond with
exactly one word: positive, negative, or neutral in lowercase”)
6406536004928.
Ignore formatting inconsistencies and use the LLM responses as they are
without additional processing
6406536004929.
Switch to a different LLM for each review in hopes of getting consistent output
without post-processing
A published solution is not available for this question yet.
**Scenario 3: Sentiment Analysis with LLMs**
**Context**
A product team wants to analyze customer feedback from app reviews to understand user
sentiment and identify common issues. Instead of manual review, they use an LLM to
automatically classify sentiment and extract key themes from review text.
**Available Data:**
• 500 app reviews (text feedback, 1-5 star ratings)
• LLM API access (e.g., OpenAI GPT, Anthropic Claude)
• Goal: Classify sentiment (positive/negative/neutral) and extract main topics
Based on the above data, answer the given subquestions.
After classifying all reviews, you want to find what percentage are negative. Which approach
correctly calculates this?
6406536004930.
Manually count negative reviews
6406536004931.
Use a DataFrame, count rows with sentiment 'negative', divide by total,
multiply by 100
6406536004932.
Ask the LLM to calculate the number of negative reviews
6406536004933.
Count only positive reviews
A published solution is not available for this question yet.
**Scenario 4 : Customer Support Chatbot with RAG**
**Context**
An e-commerce company is building a customer support chatbot that answers questions about
products, policies, and orders. Instead of training a custom model, they use RAG (Retrieval
Augmented Generation) to retrieve relevant information from their knowledge base and generate
responses.
**System Components:**
• **Knowledge Base**: 500 product manuals, FAQs, and policy documents
• **Vector Database**: Stores document embeddings for similarity search
• **LLM**: Generates responses based on retrieved context
• **Chunking Strategy**: Break documents into smaller pieces for better retrieval
**Sample Documents:**
**User Query:** "What is your return policy?"
Based on the above data, answer the given subquestions.
In a RAG system, what is the purpose of breaking documents into smaller chunks before storing
them?
6406536004934.
To save storage space in the database
6406536004935.
Match queries with relevant sections, not whole documents, for better
accuracy.
6406536004936.
To make documents easier to read for humans
6406536004937.
To reduce the cost of LLM API calls
A published solution is not available for this question yet.
**Scenario 4 : Customer Support Chatbot with RAG**
**Context**
An e-commerce company is building a customer support chatbot that answers questions about
products, policies, and orders. Instead of training a custom model, they use RAG (Retrieval
Augmented Generation) to retrieve relevant information from their knowledge base and generate
responses.
**System Components:**
• **Knowledge Base**: 500 product manuals, FAQs, and policy documents
• **Vector Database**: Stores document embeddings for similarity search
• **LLM**: Generates responses based on retrieved context
• **Chunking Strategy**: Break documents into smaller pieces for better retrieval
**Sample Documents:**
**User Query:** "What is your return policy?"
Based on the above data, answer the given subquestions.
When a user asks "What is your return policy?", which step happens FIRST in a RAG system?
6406536004938.
Generate a response directly using the LLM
6406536004939.
Convert the query to an embedding and search similar chunks
6406536004940.
Store the user’s question in the database for future use
6406536004941.
Send an email to customer support make all options of same length
A published solution is not available for this question yet.
**Scenario 4 : Customer Support Chatbot with RAG**
**Context**
An e-commerce company is building a customer support chatbot that answers questions about
products, policies, and orders. Instead of training a custom model, they use RAG (Retrieval
Augmented Generation) to retrieve relevant information from their knowledge base and generate
responses.
**System Components:**
• **Knowledge Base**: 500 product manuals, FAQs, and policy documents
• **Vector Database**: Stores document embeddings for similarity search
• **LLM**: Generates responses based on retrieved context
• **Chunking Strategy**: Break documents into smaller pieces for better retrieval
**Sample Documents:**
**User Query:** "What is your return policy?"
Based on the above data, answer the given subquestions.
The chatbot sometimes returns information from outdated product manuals even though newer
versions exist. What is the most likely cause of this issue?
6406536004942.
The LLM relies on outdated training data instead of current documents
6406536004943.
Old document chunks remain in the vector database and weren’t updated
6406536004944.
The document chunking strategy was applied incorrectly or inconsistently
6406536004945.
The embedding model is too small to capture document differences accurately
A published solution is not available for this question yet.
**Scenario 4 : Customer Support Chatbot with RAG**
**Context**
An e-commerce company is building a customer support chatbot that answers questions about
products, policies, and orders. Instead of training a custom model, they use RAG (Retrieval
Augmented Generation) to retrieve relevant information from their knowledge base and generate
responses.
**System Components:**
• **Knowledge Base**: 500 product manuals, FAQs, and policy documents
• **Vector Database**: Stores document embeddings for similarity search
• **LLM**: Generates responses based on retrieved context
• **Chunking Strategy**: Break documents into smaller pieces for better retrieval
**Sample Documents:**
**User Query:** "What is your return policy?"
Based on the above data, answer the given subquestions.
When choosing chunk size for splitting documents, which approach generally provides the best
balance for a customer support use case?
6406536004946.
Use very small chunks (1–2 sentences) for highly precise matches
6406536004947.
Use medium chunks (1–2 paragraphs, ~200–500 words) for context and focus
6406536004948.
Use very large chunks (entire documents) to provide maximum context
6406536004949.
Use randomly sized chunks to introduce variety in the database
A published solution is not available for this question yet.
**Scenario 4 : Customer Support Chatbot with RAG**
**Context**
An e-commerce company is building a customer support chatbot that answers questions about
products, policies, and orders. Instead of training a custom model, they use RAG (Retrieval
Augmented Generation) to retrieve relevant information from their knowledge base and generate
responses.
**System Components:**
• **Knowledge Base**: 500 product manuals, FAQs, and policy documents
• **Vector Database**: Stores document embeddings for similarity search
• **LLM**: Generates responses based on retrieved context
• **Chunking Strategy**: Break documents into smaller pieces for better retrieval
**Sample Documents:**
**User Query:** "What is your return policy?"
Based on the above data, answer the given subquestions.
A customer asks "Can I return my laptop if I opened the box?" The chatbot retrieves two relevant
chunks: one about return policy and one about laptop specifications. How does the LLM use these
chunks?
6406536004950.
The LLM ignores the retrieved chunks and answers using its training data
6406536004951.
The LLM uses the chunks as context to generate an informed answer
6406536004952.
The LLM copies text from the chunks verbatim without changes
6406536004953.
The LLM stores the retrieved chunks for future queries
A published solution is not available for this question yet.
