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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
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. Minimizing data movement 3. Caching and warehouse sizing
Snowpark API and Development	30%	- Python API fundamentals 1. Column operations and functions 2. Data persistence and writing results 3. DataFrame creation from tables, views, SQL - Multi-language support 1. Java and Scala API basics 2. Environment setup and dependencies
Data Transformations and Operations	35%	- User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - DataFrame manipulation 1. Filtering, sorting, grouping, aggregation 2. Selection, projection, renaming, casting 3. Joins, unions, set operations - Advanced operations 1. Pivot and unpivot transformations 2. Window functions and analytics 3. Semi-structured data processing
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. Authentication and connection settings 2. Create and configure Snowpark sessions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q111-Q116):

NEW QUESTION # 111
You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?

A. Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.
B. Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.
C. Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
D. Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.
E. Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.

Answer: A

Explanation:
Using Snowflake Anaconda channels (option C) is the recommended and most efficient way to manage Python dependencies for Snowpark stored procedures. It provides version control, dependency management, and ensures consistency across executions. Option A is highly discouraged due to security risks and maintainability issues. Option B can work, but it lacks proper dependency management and versioning. Option D is not possible, as you cannot directly modify the Snowflake compute warehouse nodes. Option E is incorrect. Task definititions does not have any mechanism to include the library

NEW QUESTION # 112
You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?

Answer: B

Explanation:
Option E is correct because it first extracts the 'items' array into a new column 'items_array' using 'withColumn' . Then, it uses the explode' function to flatten the array into rows, aliasing the exploded column as 'item'. Finally, it selects the desired fields 'item_name' , quantity') from the 'item' column using 'getltem'.

NEW QUESTION # 113
You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

Answer: B

Explanation:
Option A is correct. It correctly uses to group by 'product_id' and 'order_by' to sort by 'sale_date' within each product group. It then calculates the cumulative sum using Options B, C, D and E contain typos or incorrect function usage or order of arguments. 'cumulative_surn' is not a standard function provided.

NEW QUESTION # 114
You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A. Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
B. Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
C. Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
D. Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.
E. Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.

Answer: B

Explanation:
Option D, using 'LATERAL FLATTEN' within a SQL context, is the most efficient approach. 'LATERAL FLATTEN' is designed specifically for flattening arrays in Snowflake and can handle nested structures efficiently within SQL. By crafting a SQL statement and using session.sqr, one can leverage the power of Snowflake's SQL engine for this task. Other options involve more complex code (UDFs, RDD conversions) or are less efficient (iterative exploding).

NEW QUESTION # 115
You are tasked with building a Snowpark application that processes sensor data. The data arrives continuously and is ingested into a Snowflake table called 'RAW SENSOR DATA'. You need to create a Snowpark DataFrame that applies a user-defined function (UDF) to each row to enrich the data. The UDF, named 'ENRICH SENSOR DATA, is written in Python and resides in a stage called 'UDF STAGE. The UDF takes three arguments: 'timestamp', and 'raw_value', all of which are STRING type in Snowflake. Which of the following code snippets correctly defines and calls the UDF using Snowpark?

Answer: B

Explanation:
Option E is correct because it uses 'call_udf function which correctly calls pre existing UDF function defined in Snowflake database. Option A & D are syntactically incorrect since return_type and input_types are required by @udf decorator. Option B attempts to define a UDTF (User-Defined Table Function) which is not what's requested in the question, and the 'select' function is not used correctly in this context. Option C defines the UDF inline, which is a valid approach, but it's less efficient than calling an existing one, and it misses the point that the UDF already exists.

NEW QUESTION # 116
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