- A
PDFLoader
Why wrong: PDFLoader is for PDF documents.
- B
TextLoader
Why wrong: TextLoader is for plain text files, not CSV.
- C
TokenTextSplitter
Correct. TokenTextSplitter splits text into token-based chunks, essential for RAG.
- D
CSVLoader
Correct. CSVLoader is designed to load CSV files, matching the data format.
- E
TextSplitter
Why wrong: Incorrect. TextSplitter is an abstract base class, not a directly usable component.
Question 5 of 991
LangChain Components for CSV: CSVLoader and TokenTextSplitter
This 1Z0-1127 practice question tests your understanding of langchain and ai application development. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer wants to load a CSV file containing customer feedback and split it into chunks for a RAG pipeline. Which TWO LangChain components should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
TokenTextSplitter
Option C (TokenTextSplitter) is correct because it splits text into chunks based on token count, ensuring each chunk fits within the LLM's context window—a key requirement for RAG pipelines. Option D (CSVLoader) is correct because it is specifically designed to load CSV files, making it the appropriate choice for ingesting the customer feedback data. Option E (TextSplitter) is incorrect because it is an abstract base class and not a directly usable component; developers must use a concrete implementation like TokenTextSplitter.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
PDFLoader
Why it's wrong here
PDFLoader is for PDF documents.
- ✗
TextLoader
Why it's wrong here
TextLoader is for plain text files, not CSV.
- ✓
TokenTextSplitter
Why this is correct
Correct. TokenTextSplitter splits text into token-based chunks, essential for RAG.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
CSVLoader
Why this is correct
Correct. CSVLoader is designed to load CSV files, matching the data format.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
TextSplitter
Why it's wrong here
Incorrect. TextSplitter is an abstract base class, not a directly usable component.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often select an abstract class (TextSplitter) thinking it is a viable component, but only concrete implementations like TokenTextSplitter are usable. In a question asking for exactly two components, including the abstract class leads to an incorrect answer.
Detailed technical explanation
How to think about this question
TokenTextSplitter uses a tokenizer (e.g., from tiktoken) to count tokens accurately, ensuring chunks respect the LLM's token limit (e.g., 4096 tokens for GPT-3.5). CSVLoader internally uses Python's csv module to parse rows and columns, then converts each row into a Document object with metadata, preserving field names for downstream retrieval. In a real-world RAG pipeline, combining CSVLoader with TokenTextSplitter allows chunking feedback entries by token count while maintaining the original row structure for accurate context retrieval.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LangChain and AI Application Development — This question tests LangChain and AI Application Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: TokenTextSplitter — Option C (TokenTextSplitter) is correct because it splits text into chunks based on token count, ensuring each chunk fits within the LLM's context window—a key requirement for RAG pipelines. Option D (CSVLoader) is correct because it is specifically designed to load CSV files, making it the appropriate choice for ingesting the customer feedback data. Option E (TextSplitter) is incorrect because it is an abstract base class and not a directly usable component; developers must use a concrete implementation like TokenTextSplitter.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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