Snowflake SPS-C01: SnowPro Specialty: Snowpark

Last checked October 22, 2026 against the official Snowflake exam page.

1. Question 1Generative AI fundamentals

   A team wants a large language model to answer questions using only company documents, reducing made-up answers. Which pattern is most appropriate?

   1. ALowering the context window to zero tokens so the model draws on its pretrained weights, which keeps replies consistent
   2. BAsking the model to answer from its pretrained weights, with no source text
   3. CRetrieval-augmented generation that retrieves relevant passages and passes them to the model
   4. DFine-tuning the model on the company's logo and brand colour palette to improve replies
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   Correct answer: C

   Retrieval-augmented generation grounds answers in retrieved company content. Logos, zero context, and unsourced answers do not ground the model.

2. Question 2Snowflake Cortex AI functions

   A data analyst wants to classify customer support messages by sentiment directly in SQL, without training a model. Which Snowflake capability is most appropriate?

   1. ACreating a materialised view of the messages that refreshes on a schedule
   2. BA Cortex LLM function such as SENTIMENT or a COMPLETE prompt applied in the query
   3. CWriting a custom neural network in a spreadsheet macro to score each row
   4. DExporting the messages to a plain text file for manual reading by support team
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   Correct answer: B

   Cortex LLM functions run inside SQL without model training. Spreadsheets, manual reading, and materialised views do not perform sentiment analysis.

3. Question 3Retrieval and grounding

   In a retrieval-augmented generation pipeline, what is the role of the embedding step?

   1. AIt converts text into numerical vectors so that similar passages can be found by meaning
   2. BIt deletes duplicate documents from the corpus before indexing, keeping the newest revision of each file
   3. CIt translates the generated answer into another language using a separate machine translation model
   4. DIt sets the user's row-level access permissions on the index before each search request is run
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   Correct answer: A

   Embeddings convert text to vectors that capture meaning for similarity search. Translation, deletion, and permissions are different functions.

4. Question 4Responsible AI and governance

   A company wants to prevent Cortex AI features from exposing sensitive data to users who are not authorised to see it. Which control applies to the data the model can access?

   1. ARole-based access control and policies on the underlying tables and documents
   2. BSharing one administrator login with the AI team so its service account can read every table
   3. CHoping the model will refuse sensitive questions and keep figures private
   4. DRemoving row-level security from the source data tables for the AI feature's own service role
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   Correct answer: A

   Access controls and policies on underlying data limit what the AI can retrieve for each user. Relying on the model to refuse, removing security, and shared logins do not enforce access.

5. Question 5Cost and performance

   A generative AI feature is expensive to run because it calls a large model for simple classification tasks. What is the best change?

   1. AUse a smaller model for simple tasks and reserve the large model for complex ones
   2. BCall the model twice for every request and keep the second answer as a backup
   3. CRemove the classification feature from the product and its dashboards
   4. DUse the largest model for every task to be safe and very accurate
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   Correct answer: A

   Routing simple tasks to a smaller model reduces cost while keeping quality for complex tasks. The largest model everywhere, duplicate calls, and removing the feature are poor responses.