Snowflake DSA-C03 Certification Exam Sample Questions

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Snowflake DSA-C03 Sample Questions:

01. A feature pipeline runs in several steps, each re-reading and re-aggregating the same large base table, with short pauses between them while an external labeling service responds. On a second warehouse, analysts run ad-hoc queries that touch different tables each time and leave long idle stretches between them. The team has applied the same aggressive auto-suspend interval to both warehouses.
Which assessment of that setting is correct?
a) The interval suits both warehouses, since a suspended warehouse bills for nothing at all, and every step of either workload is short enough to absorb whatever a resume costs it.
b) It is wrong for the pipeline, whose steps re-read the same scanned data, and right for the analyst warehouse, whose queries share little.
c) It is wrong for both, since every workload pays to resume.
d) It is wrong for the analyst warehouse, whose ad-hoc queries most need a warm cache, and right for the pipeline.
 
02. An auditor asks which tables and columns one particular training run read. The job's own repository declares the sources it was written against, but the job also selects from views that another team has since rebuilt, so a run today would not resolve to the same base objects.
Which source settles the auditor's question?
a) The lineage annotations kept in the pipeline repository, which declare every source the job was designed to consult and are versioned alongside its code.
b) The Model Registry entry for the run, which stores the base tables that the training query resolved to at execution time.
c) Access History, which records the objects each query actually read, including those reached through a view.
d) A fresh profiling run of the same job, captured while it executes.
 
03. A team re-embeds a document corpus with a newer Cortex embedding model, writing the results to the same VECTOR column so that the existing table and search query need no change. The path that embeds incoming questions is not redeployed and still calls the previous model. No error is raised, each search returns the expected number of results, and answer quality falls sharply.
What accounts for the failure?
a) Query vectors and document vectors now come from different models, so the distances between them no longer express similarity even though they can still be computed.
b) The newer model orders its dimensions differently from the previous one, so the column must be rebuilt with the dimensions sorted back into their original positions before any search will work.
c) The corpus was re-embedded without being re-chunked, so the new vectors describe passages too long for the model that produced them.
d) Matching vector dimensions guarantee comparability, so the fault lies in the ranking step rather than in the embeddings.
 
04. A pipeline loads supplier contracts from a stage. The downstream table needs the counterparty name, the effective date and the notice period in days as three typed columns, so that every contract row can be joined to the vendor master. An engineer proposes running a Cortex AISQL summarization step over each contract and writing its output into a single text column for a later parse, rather than using Document AI to pull the three fields directly.
Why can the proposed step never produce what the table requires, even when a summary keeps every value?
a) A summary may leave out the counterparty, the date or the notice period altogether, so some contracts would arrive at the vendor master join with one or more of the three values missing.
b) Summarization has one output, a block of prose, whereas the table needs a named value per field; extraction with Document AI emits one output per field asked for.
c) Cortex AISQL cannot read a document while it is still on a stage, so the contracts would need loading into a table first.
d) Text columns carry no index, so the vendor master join would be too slow.
 
05. Two analysts ask Cortex Analyst for net revenue for the last quarter, a week apart. They hold the same role, with identical access to the same governed tables, and the underlying data has not been reloaded between the two questions. The two figures differ. The finance lead does not simply want a repeatable number; she needs Cortex Analyst to return the figure the business has defined as net revenue, on a basis someone can audit afterward.
What should the team put in place?
a) Lower the sampling variation on the underlying model so that the same question produces the same generated text, which will make the query it builds stable from one session to the next.
b) More worked examples of question-and-answer pairs in the prompt, giving the model additional patterns to imitate when it builds the query.
c) Identical role grants for both analysts, since two different figures over one dataset almost always reflect a row access difference rather than a difference in how the question was interpreted.
d) A curated semantic model for Cortex Analyst (the metrics, the joins and the dimensions it is permitted to use) so that the question resolves to one defined calculation rather than to whichever plausible join the model assembles that session.
 
06. A team is settling how to chunk a technical corpus before it is indexed by a Cortex Search service. One engineer argues for much larger chunks so that no passage is ever cut mid-argument; another argues for much smaller chunks so that retrieval returns only the sentence that matters.
Which two statements describe the trade-off they are weighing?
(Select TWO.)
a) A chunk covering several distinct topics produces one embedding that represents none of them sharply, so the match signal for any single topic is weakened.
b) Chunk size determines how many documents the search service is able to hold, so smaller chunks are chosen whenever the corpus is expected to outgrow the storage allocated to it.
c) A chunk narrow enough to hold a single sentence can lose the surrounding text that establishes what the sentence refers to.
d) Once the service has been rebuilt, chunk size no longer affects which passages are returned for a query.
e) Halving the chunk size doubles recall, because twice as many candidates become available to the ranking step.
 
07. A compliance team edits its policy corpus in Snowflake several times a week, and every edit is visible to anyone querying the table as soon as it is committed. The grounded assistant built on a Cortex Search service over that table keeps answering from the superseded wording for days afterwards. Nothing in the retrieval or generation configuration has changed.
What determines how quickly an edit reaches the assistant's answers?
a) The refresh cadence of the Cortex Search service, because an edit is not retrievable until the service has picked the new text up into the index it serves queries from.
b) The rate at which the compliance team edits the corpus, since a Cortex Search query reads the policy table directly at answer time.
c) The size of the context window, because a revised clause has to compete with older text already occupying the prompt.
d) The similarity threshold, which holds a revised clause below the cut until enough queries have matched it.
 
08. An assistant answering questions about an internal data platform occasionally states a retention period, a data owner and an approval step in one fluent paragraph, all of them invented. The team proposes pointing the Cortex AISQL call at a larger and more recent model, reasoning that the current one is not capable enough to get these details right.
What is wrong with that reasoning?
a) Larger models are tuned for general reasoning rather than factual recall, so the smaller model is the better choice for questions of this kind.
b) The behavior is the model producing fluent text past the point its evidence supports, so a more capable model states the same unsupported details more convincingly unless the answer is tied to passages retrieved from the corpus and the model is permitted to decline.
c) The invented details point to a corrupted prompt template, so the fix is to rebuild the template rather than to change model.
d) A more recent model would resolve it, but only once the platform's documentation has been added to its training data.
 
09. Two nightly jobs each process 50,000 rows through the same Cortex AISQL summarization operation. One runs over short chat messages; the other over case files extracted from long PDFs. The case-file job costs several times more than the chat job, which surprises the team because the row counts are identical and both jobs run the same statement shape.
What accounts for the difference?
a) Charging follows the volume of text sent to and returned by the model, and the long documents carry far more of it at the same row count.
b) The case-file job reads its text from a stage rather than from a table, and the additional input handling that the stage path involves is what separates two jobs of equal row count.
c) The longer inputs are routed to a larger model automatically, and the larger model carries the higher rate.
d) The case-file job holds its warehouse open longer, and that time is the difference.
 
10. An internal assistant answers employee questions from a document corpus that any employee can upload into, retrieving with Cortex Search. A user asks a routine policy question and the assistant returns an answer that ignores the refusal rules it was configured with and quotes the wording of its own system instruction back to the user. The Cortex Search log for that answer shows one recently uploaded document among the passages supplied.
What does this indicate about the design?
a) The uploaded corpus has been used to fine-tune the model, and that training has overwritten the refusal behavior configured at deployment time.
b) The retrieval step returned a passage with a weak relevance score, and weakly matched context is what causes a model to fall back on reciting its own instruction text.
c) Text returned by Cortex Search was treated as instruction rather than as data.
d) The system instruction was placed after the retrieved passages in the assembled prompt, so the model gave precedence to whichever block it read last.

Answers:

Question: 01
Answer: b		 Question: 02
Answer: c		 Question: 03
Answer: a		 Question: 04
Answer: b		 Question: 05
Answer: d
Question: 06
Answer: a, c		 Question: 07
Answer: a		 Question: 08
Answer: b		 Question: 09
Answer: a		 Question: 10
Answer: c

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