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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q120-Q125):

NEW QUESTION # 120
You are tasked with developing a data pipeline using Snowpark that involves reading data from multiple CSV files, performing transformations using Pandas DataFrames, and then loading the transformed data into a Snowflake table. You want to optimize the process by leveraging the capabilities of Snowpark and Pandas effectively. Which of the following approaches is the MOST efficient for creating the Snowpark DataFrame from the pandas dataframe? (Select all that apply.)

A. Read each CSV file into a Pandas DataFrame, perform transformations, concatenate all Pandas DataFrames into a single Pandas DataFrame, and then create a Snowpark DataFrame using 'session.createDataFrame()'.
B. Read each CSV file directly into a Snowpark DataFrame using 'session.read.csv()' , perform Snowpark DataFrame transformations, and then write to the Snowflake table. Avoid using Pandas DataFrames altogether.
C. Read each CSV file into a Pandas DataFrame, perform transformations, and then create a Snowpark DataFrame from each Pandas DataFrame using Union all the Snowpark DataFrames.
D. Read each CSV file into a Pandas DataFrame, perform transformations, and then create a temporary table with the result of 'session.write_pandas' with auto create table=True' .
E. Read each CSV file into a Pandas DataFrame, perform transformations, and then create a temporary table with the result of 'session.write_pandas' with auto create table=False' .

Answer: B,D

Explanation:
Option C is the most efficient when the transformations can be effectively done using Snowpark itself, bypassing Pandas entirely and leveraging Snowflake's compute power directly. Option D, while using Pandas for transformation, optimizes data transfer using the optimized 'write_pandas' function. Creating Snowpark DataFrames from Pandas DataFrames and then unioning (Option B) can be less performant due to data transfer overhead. Concatenating Pandas DataFrames and then creating a Snowpark DataFrame (Option A) can be memory-intensive. Option E is incorrect, setting will throw an error if table does not exist.

NEW QUESTION # 121
You have two Snowflake tables, 'customers' and 'orders'. The 'customers' table contains customer information, including a 'customer id' and 'region'. The 'orders' table contains order information, including 'order id', 'customer id', and 'order amount'. You need to create a Snowpark DataFrame that joins these two tables on 'customer id' and calculates the total order amount per region. However, some customers may not have any orders, and you want to include all customers in the result, with a total order amount of 0 for those without orders. Which of the following Snowpark code snippets will achieve this goal MOST efficiently, assuming 'customers_df and 'orders_ff are pre-existing Snowpark DataFrames representing the respective tables?

Answer: D

Explanation:
Option B is the most efficient because it uses 'coalesce' directly within the 'agg' function, avoiding a separate .na.fill' operation which could be less optimized in Snowpark. It handles the null values resulting from the left outer join correctly, ensuring that customers without orders have a 0 total order amount. Options A and C might work in some contexts, but are less idiomatic and potentially less efficient. Options D and E are less concise and may not be the most optimal way to express the desired logic in Snowpark.

NEW QUESTION # 122
You are developing a Snowpark application that involves creating a set of stored procedures and UDFs to process data'. To ensure proper version control and dependency management, you decide to package your Python code into a single Python Wheel file and deploy it to Snowflake. Which of the following methods are valid for deploying and utilizing this Python Wheel file within Snowflake, considering best practices for maintainability and security? (Select TWO)

A. Use the Snowsight UI to upload the Python Wheel file as a dependency for the Snowflake environment, making it available for all stored procedures and UDFs within that environment.
B. Upload the Python Wheel file to an external stage (e.g., AWS S3) and configure Snowflake to access the external stage, then reference the wheel file path in the USING' clause of the UDF or stored procedure.
C. Upload the Python Wheel file to an internal stage and directly reference it in the UDF or stored procedure definition using the 'USING' clause.
D. Create a Conda environment file (environment.yml) that specifies the Python Wheel file as a dependency and use this file to create a Snowflake environment. Then, associate the stored procedures and UDFs with that environment.
E. Use the 'snowflake-cli' to push the Python Wheel file as a package, then add the package name to the list of packages when creating the stored procedure or UDF.

Answer: D,E

Explanation:
Options D and E are the correct answers. Creating a Conda environment file and deploying it to Snowflake (Option D) allows for explicit version control and dependency management, ensuring consistent execution across environments. You need to upload the environment.yml which contains the packages and custom wheel you need to add. Using 'snowflake-cli' to push wheel as a package (Option E) is the approach for using custom packages. These are the recommended approaches. Uploading wheel files to internal or external stages and referencing them using the 'USING' clause (Options A and B) might work, but it lacks the structured dependency management provided by Conda environments. Snowsight cannot be used to directly upload wheels for environment setup (Option C).

NEW QUESTION # 123
You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A. Install the library directly within the Snowflake Notebook using '!pip install
B. Create a deployment file using setup.py, upload deployment file to stage, and create function
C. Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
D. Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import
E. Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.

Answer: E

Explanation:
Snowflake Notebooks primarily use conda environment specification files ('environment.yml') (B) to manage dependencies. You specify the required libraries in the 'environment.ymr file, upload it to a stage, and use it when creating or updating the Notebook environment. Uploading raw .pV files (A) might work for simple modules, but lacks dependency management. Using '!pip install' (C and E) directly in the notebook is not the intended way to manage dependencies in Snowflake Notebooks for production scenarios and might not persist across sessions.

NEW QUESTION # 124
A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?

A. Increase the Auto Suspend value from 5 minutes to 30 minutes. This will ensure that the warehouse remains active for a longer period, preventing frequent auto- suspends and subsequent resume operations.
B. Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.
C. Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
D. Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
E. Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.

Answer: A,E

Explanation:
The 'Scaling Up' and 'Scaling Down' thrashing is likely caused by the warehouse suspending too quickly, leading to constant restarts as new requests arrive. Increasing the Auto Suspend time (Option A) prevents this frequent cycling. Increasing the MIN CLUSTER COUNT (Option B) makes more resources readily available, helping the warehouse respond faster to spikes in demand and reducing the need for scaling up. Workload management (Option C) is a good practice but may not directly address the root cause of the instability. Reducing MAX_CLUSTER_COUNT (Option D) could worsen the problem by limiting the warehouse's ability to handle peak loads. Changing to 'ECONOMY' scaling policy (Option E) would prioritize cost over performance, which is counter to improving performance.

NEW QUESTION # 125
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