100+ Free SnowPro Advanced Data Scientist Practice Questions

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Question 1

Score: 0/0

Which Snowpark ML class provides one-hot encoding of categorical features as a Snowflake-native preprocessing step?

snowflake.ml.modeling.preprocessing.OneHotEncoder

sklearn.preprocessing.OneHotEncoder applied via to_pandas()

snowflake.cortex.ENCODE

snowflake.snowpark.functions.one_hot

to track

2026 Statistics

Key Facts: SnowPro Advanced Data Scientist Exam

Live Exam Questions

Snowflake

115 min

Time Limit

Snowflake

750/1000

Passing Score

Scaled

$375

Exam Fee

$300 in India

30%

Largest Domain

Feature Engineering

2 years

Certification Valid

Renew via SnowPro Core

SnowPro Advanced Data Scientist (DSA-C03) is a 65-question Snowflake exam delivered in 115 minutes with a passing scaled score of 750/1000. The 2026 blueprint covers Data Science Concepts (15%), Data Pipelining (19%), Data Preparation and Feature Engineering (30%), Model Development (20%), and Model Deployment (16%). Snowflake requires an active SnowPro Core certification as a prerequisite and recommends 2+ years of hands-on data science experience with Snowflake. Exam fee is $375 USD per attempt ($300 USD in India). Certification is valid for 2 years; renewal requires holding an active SnowPro Core certification.

Sample SnowPro Advanced Data Scientist Practice Questions

Try these sample questions to test your SnowPro Advanced Data Scientist exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which Snowpark ML class provides one-hot encoding of categorical features as a Snowflake-native preprocessing step?

A.snowflake.ml.modeling.preprocessing.OneHotEncoder

B.sklearn.preprocessing.OneHotEncoder applied via to_pandas()

C.snowflake.cortex.ENCODE

D.snowflake.snowpark.functions.one_hot

Explanation: snowflake.ml.modeling.preprocessing.OneHotEncoder is the Snowpark ML preprocessing transformer that runs natively against Snowflake compute and avoids pulling data to the client. The scikit-learn version requires to_pandas(), which forces a costly data movement to a Python process. There is no Cortex.ENCODE or snowpark.functions.one_hot helper for categorical encoding.

2A data scientist needs to generate 1024-dimensional embeddings for retrieval-augmented generation. Which Cortex LLM Function should be used?

A.EMBED_TEXT_768

B.EMBED_TEXT_1024

C.COMPLETE

D.EXTRACT_ANSWER

Explanation: EMBED_TEXT_1024 returns a 1024-dimension VECTOR per input string, suited for RAG pipelines that need higher-fidelity semantic similarity. EMBED_TEXT_768 returns a smaller 768-dim vector. COMPLETE generates text and EXTRACT_ANSWER does extractive QA against a passage.

3Which Snowflake feature lets you reproduce a training dataset exactly as it existed two days ago without doubling storage cost?

A.Zero-Copy Cloning combined with Time Travel

B.Fail-safe and a manual snapshot

C.Materialized view refresh history

D.COPY INTO of a historical Parquet stage

Explanation: Zero-Copy Cloning a table AT or BEFORE a Time Travel timestamp produces a logical, metadata-only copy that references the same micro-partitions, so storage cost only increases when the clone diverges. Fail-safe is non-configurable disaster recovery and not user-queryable. Materialized view history does not snapshot training tables, and re-loading from Parquet adds latency and cost.

4Which compute resource is required to run a containerized PyTorch training job with GPU acceleration inside Snowflake?

A.A Snowpark-optimized warehouse

B.A Snowpark Container Services compute pool with a GPU instance family

C.An XS standard warehouse with auto-scaling enabled

D.A serverless Tasks compute pool

Explanation: GPU training in Snowflake runs on Snowpark Container Services, which uses compute pools that can be configured with GPU instance families. Snowpark-optimized warehouses provide larger memory but are CPU-only. Standard warehouses are not GPU-enabled, and serverless Tasks share managed compute that is also CPU-only.

5What does VECTOR_COSINE_SIMILARITY return between two embedding vectors?

A.A signed Euclidean distance value

B.A similarity score between -1 and 1 where higher is more similar

C.A Manhattan distance between coordinates

D.An integer rank position

Explanation: VECTOR_COSINE_SIMILARITY returns a value between -1 and 1, with 1 indicating identical direction and -1 the opposite. Snowflake also provides VECTOR_L2_DISTANCE for Euclidean and VECTOR_INNER_PRODUCT for dot product, but cosine similarity is the most common metric for normalized embeddings.

6Which Snowpark ML class trains a gradient-boosted classifier natively on Snowflake compute?

A.snowflake.ml.modeling.classification.XGBClassifier

B.snowflake.ml.cortex.GradientBoosting

C.snowflake.snowpark.ml.GBM

D.snowflake.ml.tuning.GBClassifier

Explanation: snowflake.ml.modeling.classification.XGBClassifier is a Snowpark ML wrapper around XGBoost that distributes fit/predict on Snowflake warehouses. The other names are not real Snowpark ML APIs.

7What is the primary purpose of the Snowpark ML Model Registry?

A.Storing raw training datasets

B.Versioning and deploying ML models with metadata in Snowflake

C.Replacing the warehouse query cache

D.Scheduling Streams and Tasks

Explanation: The Model Registry provides versioned storage of ML model artifacts plus metadata (signature, metrics, dependencies) and supports deployment as Snowflake functions or to Snowpark Container Services. Datasets are stored in tables, the warehouse query cache is unrelated, and Streams/Tasks are pipeline objects.

8A team wants to convert a long passage into a concise abstract using Snowflake-managed LLMs. Which Cortex function should they call?

A.SUMMARIZE

B.CLASSIFY_TEXT

C.EXTRACT_ANSWER

D.COMPLETE with a 'translate' prompt

Explanation: SNOWFLAKE.CORTEX.SUMMARIZE produces a concise summary of a passed string. CLASSIFY_TEXT assigns labels, EXTRACT_ANSWER answers questions over a passage, and COMPLETE is general-purpose generation that would require careful prompt engineering for summarization quality.

9Which Cortex capability is purpose-built for retrieval-augmented generation with managed indexing of documents?

A.Cortex Analyst

B.Cortex Search

C.Cortex Fine-tuning

D.Document AI

Explanation: Cortex Search is a managed retrieval service that builds and maintains a hybrid lexical/semantic index for RAG. Cortex Analyst converts natural language to SQL, Cortex Fine-tuning customizes base LLMs, and Document AI extracts structured data from unstructured documents.

10You need to convert a Snowflake table to a pandas DataFrame for local exploration. Which approach minimizes egress?

A.Sample the table with TABLESAMPLE before calling to_pandas()

B.Use COPY INTO @stage and download all CSVs

C.Materialize the entire table as a view and read it

D.Call to_pandas() with no filtering

Explanation: Sampling with TABLESAMPLE BERNOULLI or SYSTEM reduces the number of rows transferred before to_pandas(), keeping egress and memory low. The other options either transfer all rows, do not reduce data, or add unnecessary stages.

About the SnowPro Advanced Data Scientist Exam

The SnowPro Advanced: Data Scientist (DSA-C03) certification validates the ability to apply advanced data science methodologies on the Snowflake AI Data Cloud. It covers Snowpark ML modeling and registry, Snowflake Cortex LLM Functions (COMPLETE, EMBED_TEXT_1024, CLASSIFY_TEXT, SUMMARIZE), feature engineering with Snowpark DataFrames, model deployment via vectorized UDFs and Snowpark Container Services, and ML observability with Data Metric Functions.

Assessment

Multiple-choice and multiple-select items on the live exam

Time Limit

115 minutes

Passing Score

750/1000 (scaled)

Exam Fee

$375 USD (Snowflake / Pearson VUE)

SnowPro Advanced Data Scientist Exam Content Outline

15%

Data Science Concepts

Supervised vs unsupervised learning, classification, regression, clustering, evaluation metrics (precision, recall, F1, AUC), bias-variance tradeoff, and ML lifecycle on Snowflake.

19%

Data Pipelining

Data ingestion with Snowpipe and Snowpipe Streaming, Streams and Tasks for orchestration, dynamic tables, external tables and Iceberg tables, semi-structured handling for ML inputs.

30%

Data Preparation and Feature Engineering

Snowpark DataFrame API, snowflake.ml.modeling.preprocessing (OneHotEncoder, StandardScaler), missing value handling, feature stores (entities, feature views), Time Travel and Zero-Copy Cloning for dataset versioning.

20%

Model Development

Snowpark ML Modeling (RandomForestClassifier, XGBClassifier, LogisticRegression), Cortex LLM Functions (COMPLETE, EMBED_TEXT_1024, CLASSIFY_TEXT, SENTIMENT, EXTRACT_ANSWER), Cortex Fine-tuning, Cortex Search, Document AI, Snowflake Notebooks and Streamlit in Snowflake.

16%

Model Deployment

Model Registry (log_model, get_model, deploy), vectorized UDFs vs Snowpark Container Services with GPU compute pools, Snowpark-optimized warehouses, REST endpoints, monitoring with DMFs and EVENT_TABLE.

How to Pass the SnowPro Advanced Data Scientist Exam

What You Need to Know

Passing score: 750/1000 (scaled)
Assessment: Multiple-choice and multiple-select items on the live exam
Time limit: 115 minutes
Exam fee: $375 USD

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

SnowPro Advanced Data Scientist Study Tips from Top Performers

1Study to the official 15/19/30/20/16 weighting and prioritize feature engineering and model development together because most exam scenarios blend them.

2Build a Snowpark ML pipeline end to end: ingest, preprocess with OneHotEncoder/StandardScaler, train an XGBClassifier, log it to the Model Registry, then deploy as a vectorized UDF.

3Know when to choose Snowpark Container Services with GPU compute pools versus a Snowpark-optimized warehouse and a vectorized UDF for inference.

4Practice every Cortex LLM Function: COMPLETE, SUMMARIZE, TRANSLATE, SENTIMENT, CLASSIFY_TEXT, EXTRACT_ANSWER, EMBED_TEXT_768, EMBED_TEXT_1024.

5Understand RAG patterns with Cortex Search, Vector data type, and VECTOR_COSINE_SIMILARITY rather than only memorizing function names.

6Use Time Travel and Zero-Copy Cloning to version training datasets and reproduce experiments without doubling storage cost.

7Run timed practice sets because 115 minutes for 65 scenario-driven items leaves limited time for over-analysis.

8Confirm SnowPro Core (COF-C02) is active before scheduling and remember the 2-year validity and 7-day retake rule.

Frequently Asked Questions

What is the format of the SnowPro Advanced Data Scientist exam?

DSA-C03 is a 65-question exam delivered in 115 minutes through Pearson VUE, available online proctored or onsite. Question types include multiple choice and multiple select. Snowflake may include unscored experimental items that do not affect your final score. Results are reported on a 0-1000 scaled scoring system.

What score do I need to pass DSA-C03?

You need a scaled score of 750 out of 1000 to pass. Because Snowflake uses scaled scoring, this does not translate directly to a fixed percent-correct target. Strong candidates aim for consistent performance across all five blueprint domains rather than over-investing in a single area like Cortex or Snowpark ML.

What are the official DSA-C03 domain weights?

The 2026 blueprint covers Data Science Concepts (15%), Data Pipelining (19%), Data Preparation and Feature Engineering (30%), Model Development (20%), and Model Deployment (16%). Feature engineering and preparation is the heaviest domain, reflecting how much real production ML work happens in data wrangling on Snowpark DataFrames.

Do I need a prerequisite to take DSA-C03?

Yes. Snowflake requires an active SnowPro Core certification (COF-C02) as a prerequisite for all SnowPro Advanced exams, including Data Scientist. Snowflake also recommends 2+ years of hands-on production data science experience with Snowflake, including Snowpark and at least familiarity with Snowflake Cortex.

How does the 2026 DSA-C03 differ from the older DSA-C02?

DSA-C03 was updated to align with the Snowflake AI Data Cloud. Domains were reorganized from five to a slightly different structure with heavier emphasis on Snowflake Cortex LLM Functions, Cortex Search for RAG, Cortex Analyst, Document AI, Cortex Fine-tuning, and the Snowpark ML Model Registry. Vector data type and VECTOR_COSINE_SIMILARITY for embeddings are now in scope.

How much does the exam cost and what is the retake policy?

Advanced exams cost $375 USD per attempt, with discounted pricing of $300 USD for candidates testing in India. After a failed attempt, you must wait 7 calendar days before retaking. Snowflake allows up to 4 retakes of the same exam within a 12-month period, and each retake requires full payment.

How should I study for SnowPro Advanced Data Scientist?

Anchor your prep on the official blueprint and spend the most time on feature engineering (30%) and model development (20%). Build hands-on Snowpark ML pipelines, practice using Cortex LLM Functions and Cortex Search, deploy a model with the Model Registry, and run scenario-based timed practice. Most candidates need 80-150 hours of study depending on prior Snowpark and ML experience.