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Question 1

Score: 0/0

Which method is used to create a Snowpark Session in Python?

snowflake.connector.connect(connection_parameters)

Session.builder.configs(connection_parameters).create()

Session.create(connection_parameters)

SnowparkSession.new(connection_parameters)

to track

2026 Statistics

Key Facts: SnowPro Specialty: Snowpark Exam

Exam Questions

Snowflake (verify on official site)

750/1000

Passing Score

Snowflake (scaled)

115 min

Exam Duration

Snowflake

$375

Exam Fee (USD)

Snowflake

35%

Data Transformations

Largest domain

2 years

Certification Validity

Snowflake

The SnowPro Specialty: Snowpark exam has 65 questions in 115 minutes with a passing score of 750/1000. Domains: Snowpark Concepts, Snowpark API for Python, Data Transformations, and Performance Optimization. Requires SnowPro Core. Certification valid for 2 years. Exam fee is $375 USD via Pearson VUE (online proctored or test center).

Sample SnowPro Specialty: Snowpark Practice Questions

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

1Which method is used to create a Snowpark Session in Python?

A.snowflake.connector.connect(connection_parameters)

B.Session.builder.configs(connection_parameters).create()

C.Session.create(connection_parameters)

D.SnowparkSession.new(connection_parameters)

Explanation: In Snowpark for Python, a Session is created using the builder pattern: Session.builder.configs(connection_parameters).create(). The connection_parameters dictionary typically contains account, user, password (or authenticator), role, warehouse, database, and schema. Session is the entry point for all Snowpark operations.

2Snowpark DataFrames use which evaluation strategy?

A.Eager evaluation

B.Lazy evaluation

C.Speculative evaluation

D.Concurrent evaluation

Explanation: Snowpark DataFrames use lazy evaluation. Transformations like select, filter, and join build a logical query plan without executing it. The plan is only executed when an action is invoked (e.g., collect, show, count, to_pandas). This allows Snowflake's query optimizer to combine and optimize transformations into a single SQL statement.

3Which Snowpark DataFrame method triggers query execution and returns results to the client as a list of Row objects?

A.filter()

B.select()

C.collect()

D.withColumn()

Explanation: collect() is an action that triggers execution of the DataFrame's underlying SQL query and returns all results to the client as a list of Row objects. filter, select, and withColumn are transformations that are lazily evaluated and do not trigger execution.

4What is the purpose of the @udf decorator in Snowpark for Python?

A.To register a Python function as a Snowflake user-defined function

B.To cache a function result

C.To encrypt the function code

D.To schedule the function as a task

Explanation: The @udf decorator from snowflake.snowpark.functions registers a Python function as a Snowflake user-defined function (UDF). The function code is uploaded to a stage and registered so it can be called from SQL or Snowpark DataFrames. Optional parameters include name, return_type, input_types, and packages.

5Which Snowpark API allows pandas-style DataFrame operations to run on Snowflake compute?

A.Snowpark pandas API (Modin)

B.SnowSQL pandas extension

C.DataFrame.to_pandas()

D.Snowflake Connector pandas

Explanation: Snowpark pandas API (also called pandas-on-Snowflake) is built on Modin and lets users write standard pandas code that executes as SQL on Snowflake compute. This avoids pulling data to the client. It is imported via 'import modin.pandas as pd' after installing snowflake-snowpark-python[modin].

6Which warehouse type provides 16x more memory per node and is required for memory-intensive Snowpark workloads?

A.STANDARD

B.SNOWPARK_OPTIMIZED

C.HIGH_MEMORY

D.X-LARGE

Explanation: Snowpark-optimized warehouses (WAREHOUSE_TYPE = SNOWPARK_OPTIMIZED) provide 16x more memory and 10x more local cache per node compared to STANDARD warehouses. They are designed for memory-intensive Snowpark workloads such as large UDF/UDTF execution, ML training, and stored procedures with significant in-memory data. Minimum size is MEDIUM.

7What is the minimum warehouse size required for a Snowpark-optimized warehouse?

A.X-Small

B.Small

C.Medium

D.Large

Explanation: Snowpark-optimized warehouses have a minimum size of MEDIUM. They cannot be created at X-Small or Small sizes because the high-memory hardware requires at least the MEDIUM tier of compute resources.

8Which DataFrame method renames an existing column?

A.renameColumn()

B.withColumnRenamed()

C.alias()

D.columnRename()

Explanation: DataFrame.withColumnRenamed(existing, new) renames an existing column in the DataFrame. Note that select() with col().alias() can also rename columns within a select expression, but withColumnRenamed is the explicit rename method.

9Which method removes one or more columns from a DataFrame?

A.remove()

B.drop()

C.exclude()

D.filter()

Explanation: DataFrame.drop(*cols) returns a new DataFrame with the specified columns removed. The columns can be passed as column names or Column objects. drop is a transformation and is lazily evaluated.

10How can you inspect the SQL that a Snowpark DataFrame will execute?

A.DataFrame.toSQL()

B.DataFrame.queries

C.DataFrame.compile()

D.DataFrame.printSchema()

Explanation: DataFrame.queries returns a dictionary containing the SQL queries that the DataFrame will execute (under the 'queries' key) and any post-actions (under 'post_actions'). This is useful for debugging and validating the generated SQL before triggering execution.

About the SnowPro Specialty: Snowpark Exam

The SnowPro Specialty: Snowpark certification validates advanced skills using the Snowpark API on Snowflake. It tests DataFrame operations, sessions, lazy evaluation, UDFs and stored procedures (including vectorized UDFs and UDTFs), Snowpark-optimized warehouses, Snowpark Container Services, Snowpipe Streaming with Dynamic Tables, debugging, and performance tuning. SnowPro Core is required as a prerequisite.

Questions

65 scored questions

Time Limit

115 minutes

Passing Score

750/1000 (scaled)

Exam Fee

$375 (Snowflake / Pearson VUE)

SnowPro Specialty: Snowpark Exam Content Outline

15%

Snowpark Concepts

Sessions (Session.builder.configs().create()), DataFrames vs SQL, lazy vs eager evaluation, actions vs transformations, prerequisites, and certification context

30%

Snowpark API for Python

DataFrame operations (select, filter, withColumn, drop, join, group_by/agg, union/intersect/except, distinct, order_by, limit), Snowpark pandas (Modin), Streamlit in Snowflake, Snowflake Notebooks

35%

Data Transformations

UDFs (scalar, vectorized, UDTFs, Anaconda packages, custom imports), stored procedures (anonymous, EXECUTE AS OWNER vs CALLER), data ingestion (read.csv/json/parquet/orc/avro), Snowpark Container Services (compute pools, service specs, image registry, jobs vs services), Snowpipe Streaming and Dynamic Tables

20%

Performance Optimization

Snowpark-optimized warehouses, cache_result, query tagging, sql_simplifier_enabled, predicate pushdown, partition pruning, async execution, debugging via DataFrame.queries and DataFrame.explain()

How to Pass the SnowPro Specialty: Snowpark Exam

What You Need to Know

Passing score: 750/1000 (scaled)
Exam length: 65 questions
Time limit: 115 minutes
Exam fee: $375

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 Specialty: Snowpark Study Tips from Top Performers

1Practice writing Snowpark Python code daily — Session.builder, DataFrame transformations, and actions

2Memorize the difference between transformations (lazy) and actions (collect/show/count/take/to_pandas)

3Know when to use Snowpark-optimized warehouses (memory-intensive UDFs, ML training)

4Understand Snowpark Container Services concepts: compute pool INSTANCE_FAMILY (CPU vs GPU), service specs, image registry

5Master debugging tools: DataFrame.queries to see SQL, DataFrame.explain() for plans

6Learn the @udf and @vectorized decorators and how to declare packages and imports

7Practice Dynamic Tables with TARGET_LAG over a Snowpipe Streaming source — a common streaming pattern

Frequently Asked Questions

What is the SnowPro Specialty: Snowpark exam?

The SnowPro Specialty: Snowpark exam validates advanced skills using the Snowpark API on Snowflake. It tests DataFrame operations, sessions, UDFs, stored procedures, Snowpark-optimized warehouses, Snowpark Container Services, Snowpipe Streaming, performance tuning, and debugging across Python (primary), Java, and Scala.

How many questions are on the SnowPro Specialty: Snowpark exam?

The exam has 65 multiple-choice and multi-select questions to be completed in 115 minutes. The passing score is 750 out of 1000 (scaled). Some sources report variations (55 questions / 85 minutes); always verify on snowflake.com/certifications before scheduling.

Are there prerequisites for the SnowPro Specialty: Snowpark exam?

Yes. SnowPro Core Certification is required as a prerequisite. Additionally, Snowflake recommends 1+ years of hands-on Snowpark experience (Python preferred), familiarity with the Snowpark API, and an understanding of client-side vs server-side data operations.

What is the largest domain on the SnowPro Specialty: Snowpark exam?

Data Transformations is the largest domain at 35%. It covers UDFs (including vectorized UDFs and UDTFs), stored procedures, data ingestion via session.read.*, Snowpark Container Services, Snowpipe Streaming, and Dynamic Tables for streaming aggregations.

How should I prepare for the SnowPro Specialty: Snowpark exam?

Plan 40-60 hours of hands-on practice over 4-8 weeks. Build sample Snowpark pipelines in Python, register UDFs with Anaconda packages, deploy a small SPCS service, and create a Dynamic Table over a Snowpipe Streaming target. Combine the official Snowflake learning paths with 100+ practice questions and aim for 80%+ on practice tests.

What jobs can I get with SnowPro Specialty: Snowpark certification?

The credential supports roles like Snowflake Data Engineer, Analytics Engineer, ML Engineer (on Snowflake), Snowpark Developer, and Cloud Data Platform Engineer. It pairs well with SnowPro Advanced: Data Engineer for senior data engineering positions.