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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q119-Q124):

NEW QUESTION # 119
You have a Snowpark DataFrame named containing customer data. You want to calculate the average order value for each customer and rank them based on this average. The 'customer_df contains columns 'customer_id' and 'order_value'. Which of the following Snowpark code snippets correctly performs this task, returning a new DataFrame with 'customer_id', 'average_order_value', and 'rank' columns?

Answer: D

Explanation:
Option E is the correct answer because it uses 'Window.orderBy' without partitioning (partitionBy) to create a ranking across all customers based on their average order value. The window specification is ordered by the average order value in descending order and the 'rank()' function is applied over this window. Option A & B incorrectly partitions by customer id. Option C uses the function 'mean' instead of 'avg', which are synonyms, but is using 'row_number' incorrectly when a standard ranking is required Option D does not apply 'rank()' over a window specification and would throw an error since rank function need to apply on window function.

NEW QUESTION # 120
Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?

A. Use two separate 'withColumn()' transformations followed by two 'explode()' operations to flatten each array independently.
B. Use a 'LEFT OUTER JOIN LATERAL FLATTEN' to flatten 'product_ids' and 'user_ids', and then use 'nvl()' or 'coalesce()' to handle cases where one or both arrays are missing.
C. Use 'iff()' or 'case when' and and other relevant function to handle exception cases.
D. Convert the 'product_ids' and 'user_ids' arrays to strings using and before applying 'explode'.
E. Use function to dynamically create an array of indices for the 'explode' function and use and function to handle missing values

Answer: B,C,E

Explanation:
Options A, C, and E are correct. A: Using and along with ensures dynamic index array generation and graceful handling of missing or non-numeric dat a). C: Using 'LEFT OUTER JOIN LATERAL FLATTEN' allows both 'product_ids' and 'user_ids' arrays to be flattened, even if one or both are missing. The LEFT JOIN ensures all rows from the original data are kept. The use of handles cases where either array is null or empty, preventing errors and providing default values. E:Using or 'case when' handles exception and different length of arrays.

NEW QUESTION # 121
You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
B. Broadcast the DataFrame to all compute nodes before applying the UDF.
C. Convert the DataFrame to a Pandas DataFrame before applying the UDF.
D. Increase the number of UDF worker threads within the UDF definition.
E. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.

Answer: A

Explanation:
Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.

NEW QUESTION # 122
A data engineer has developed a Snowpark Python stored procedure, 'calculate daily summary', that processes sales data and generates a daily summary table. The procedure takes a date string as input and writes the summary to a table named 'DAILY SALES SUMMARY'. The engineer needs to operationalize this stored procedure to run automatically every day at midnight. Which of the following approaches is the MOST efficient and reliable way to schedule and execute this Snowpark stored procedure in Snowflake?

A. Use the Snowflake web interface to manually execute the stored procedure every day at midnight.
B. Create a Python script outside of Snowflake that uses the Snowflake Connector for Python to connect to Snowflake and execute the stored procedure using and schedule it using a system-level scheduler (e.g., cron or Windows Task Scheduler).
C. Develop a Snowpark UDF that wraps the stored procedure call and create a scheduled job in a third-party orchestration tool (e.g., Airflow, Prefect) to call the UDF.
D. Use a Snowpipe to trigger the stored procedure when new data arrives in the staging area.
E. Create a Snowflake Task that calls the stored procedure with a cron schedule that triggers the task every day at midnight. Ensure the task has a warehouse associated with it.

Answer: E

Explanation:
Snowflake Tasks provide a native and reliable way to schedule and execute stored procedures directly within Snowflake. Using a task eliminates the need for external schedulers and dependencies. Option A is manual and not scalable. Option B introduces external dependencies and potential security concerns. Option D adds an unnecessary layer of complexity with the UDF. Option E is triggered by data arrival and not a scheduled timeframe, also not intended for direct execution of stored procedures. Snowflake tasks are designed specifically for this purpose.

NEW QUESTION # 123
You have a Snowflake view named 'SALES SUMMARY VW' that joins several large tables and performs complex aggregations. You need to create a Snowpark DataFrame from this view Which of the following considerations are MOST important to ensure optimal performance and resource utilization when working with this DataFrame?

A. Leverage Snowpark's lazy evaluation and pushdown optimization capabilities by performing transformations and filtering on the DataFrame before executing actions.
B. Always specify the schema explicitly when creating the DataFrame from the view to prevent Snowpark from inferring it.
C. Use 'session.sql()' with the view's definition to create the DataFrame, as this is generally faster than 'session.table()' for views.
D. Avoid calling 'collect()' or 'toPandas()' on the entire DataFrame unless absolutely necessary, as these operations bring all data into the client's memory.
E. Materialize the view into a temporary table using 'CREATE TEMPORARY TABLE AS SELECT FROM SALES SUMMARY VW before creating the Snowpark DataFrame.

Answer: A,D

Explanation:
Options A and C are crucial. Option A highlights the danger of bringing large datasets into the client environment. Option C emphasizes the importance of Snowpark's core principles for efficiency. Option B is incorrect; Snowpark can efficiently infer the schema. Option D is generally less optimal than using 'session.table()' directly for views because 'session.table()' provides more opportunities for optimization by Snowpark. Option E materializes the view which would consume storage and may not be ideal unless intermediate results needs to be re-used multiple times. Furthermore temporary tables are only viable in current session context

NEW QUESTION # 124
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