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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformations and Operations	35%	- User-defined logic 1. Stored procedures with Snowpark 2. UDFs, UDAFs, UDTFs - DataFrame manipulation 1. Joins, unions, set operations 2. Selection, projection, renaming, casting 3. Filtering, sorting, grouping, aggregation - Advanced operations 1. Semi-structured data processing 2. Pivot and unpivot transformations 3. Window functions and analytics
Performance and Best Practices	10%	- Security and governance 1. Data protection and compliance 2. Access control and permissions - Optimization techniques 1. Minimizing data movement 2. Query pushdown and execution plans 3. Caching and warehouse sizing
Snowpark Concepts and Architecture	25%	- Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions - Snowpark architecture and execution model 1. Lazy evaluation and DAG execution 2. Client-side vs server-side processing 3. Transformations vs actions
Snowpark API and Development	30%	- Python API fundamentals 1. Data persistence and writing results 2. Column operations and functions 3. DataFrame creation from tables, views, SQL - Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q68-Q73):

NEW QUESTION # 68
You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A. Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.
B. Use the 'exceptAll' to ensure that there are no schema evolution issues.
C. Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
D. Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.
E. Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.

Answer: A,E

Explanation:
Approaches A and D are the most suitable for handling schema evolution during an upsert operation. Approach A involves dynamically generating the SQL WERGE statement by inspecting the schemas of both the target table and the staging DataFrame. This ensures that only the common columns are included in the update and insert clauses, preventing errors due to missing columns. Approach D suggests projecting the staging DataFrame to only include the columns that exist in the target table using DataFrame.select' . This effectively harmonizes the schema of the staging data with the target table's schema, avoiding issues during the 'merge' operation. While Snowflake does have some schema evolution capabilities, explicitly handling it in the code provides more control and predictability.

NEW QUESTION # 69
You are tasked with creating a Snowpark DataFrame from a Python list of tuples. Each tuple represents a customer record with the following structure: '(customer_id, signup_date, The 'customer _ id' should be an integer, 'signup_date' should be a date, and should be a decimal. You want to define the schema explicitly for type safety and performance. Which of the following code snippets correctly defines the schema and creates the Snowpark DataFrame?

Answer: C

Explanation:
Option A correctly defines the schema using 'StructType', 'StructField', 'Integer Type', 'DateType' , and 'DecimalType' . It also specifies the precision and scale for the 'DecimalType' which is important for accurately representing monetary values. The date values in the data are also compatible with DateType. The other options use incorrect data types for the last_purchase_amount (FloatType, DoubleType, StringType) or don't specify precision and scale for the DecimalType. Note that Snowflake DateType only accepts values formatted as YYYY-MM-DD'.

NEW QUESTION # 70
A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

A. The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.
B. Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.
C. The 'eager_execution' session parameter is set to 'True'.
D. Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
E. Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.

Answer: D,E

Explanation:
Snowpark employs lazy evaluation, which means transformations are not executed until an action is performed on the DataFrame. This allows Snowflake to optimize the entire query plan before execution. Setting 'eager_execution' to True does NOT exist in Snowpark Python. Data size exceeding Snowflake's limits would result in an error, not skipped transformations.

NEW QUESTION # 71
A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?

A. GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
B. GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
C. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
D. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
E. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,

Answer: E

Explanation:
The 'USAGE privilege on the database and schema is required for the role to discover (see) the UDFs and UDTFs. The 'EXECUTE privilege on the functions themselves is required to execute them. 'ALL PRIVILEGES' is an overly permissive grant and not the minimum required. Option D is missing the execute privilege. Option E is missing USAGE on Database and Schema.

NEW QUESTION # 72
You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

A. The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.
B. The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'
C. The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.
D. The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.
E. The Anaconda environment is not activated. Activate the environment using 'conda activate

Answer: D,E

Explanation:
The most common reasons for 'ModuleNotFoundErroff are that the Anaconda environment is not activated (B), meaning the Python interpreter doesn't know where to find the installed packages, or the package was installed in a different environment (D) than the one being used. While Python version compatibility (A) and Snowflake driver (C) can cause issues, they usually manifest as different errors. A misconfigured account identifier (E) would prevent a connection to Snowflake, but wouldn't directly cause a 'ModuleNotFoundError' for the Snowpark library itself.

NEW QUESTION # 73
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