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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowpark Concepts and Architecture	25%	- Snowpark architecture and execution model 1. Client-side vs server-side processing 2. Transformations vs actions 3. Lazy evaluation and DAG execution - Session management and connection 1. Authentication and connection settings 2. Create and configure Snowpark sessions
Topic 2: Performance and Best Practices	10%	- Optimization techniques 1. Query pushdown and execution plans 2. Minimizing data movement 3. Caching and warehouse sizing - Security and governance 1. Data protection and compliance 2. Access control and permissions
Topic 3: Snowpark API and Development	30%	- Multi-language support 1. Environment setup and dependencies 2. Java and Scala API basics - Python API fundamentals 1. Data persistence and writing results 2. Column operations and functions 3. DataFrame creation from tables, views, SQL
Topic 4: 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. Semi-structured data processing 2. Window functions and analytics 3. Pivot and unpivot transformations

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q145-Q150):

NEW QUESTION # 145
You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

Answer: E

Explanation:
Explanation:Option A provides the MOST accurate and efficient way to sample 1% of the data using , seed-42)'. The 'frae parameter directly specifies the fraction of rows to sample. 'seed' ensures consistent sampling across multiple runs. Option B is inefficient because it calculates the total count of rows before limiting, which scans the entire table. Option C, uses 'randomSplit' , but it splits the data into two new dataframes. Option D, utilizes Snowflake's SQL sampling feature via 'session.sqr , is a valid alternative, but less idiomatic when using Snowpark API directly. Option E, the parameter 'n' is deprecated and has been removed. 'sample' requires 'frae parameter.

NEW QUESTION # 146
Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A. Change 'FloatType' to 'DoubleType' as it provides more precision.
B. Use to explicitly declare the Pandas dependency.
C. Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.
D. Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.
E. Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.

Answer: C,D,E

Explanation:
Clustering by 'ticker_symbol' significantly optimizes the 'groupBy' operation by organizing data physically for efficient retrieval. Caching the result before writing to a table is crucial if the intermediate result 'result' will be used multiple times, avoiding redundant computations. Repartitioning by 'ticker_symbol' before grouping helps distribute data evenly, improving parallelism and locality. While Pandas is generally available, explicitly declaring it might be necessary in certain isolated environments, so B can be considered a good practice, but not as impactful. Changing FloatType to DoubleType (C) will not lead to performnace improvement. It only changes precision level which is not required here.

NEW QUESTION # 147
Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A. Add 'session.close()' at the end of the main function.
B. Define 'session' as a global variable before calling 'session.sproc.register' .
C. Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
D. Add in sproc.register.
E. Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.

Answer: E

Explanation:
Option C is the correct solution. The 'session.sproc.registefl method should be called outside of the 'main' function definition. The registration process is a separate step from the function definition itself. The 'sproc.register' will take the function definition and create an object in Snowflake. Since this must be outside of the function it needs to be outside the main function. All other options would not solve the error with the registration.

NEW QUESTION # 148
You are developing a Snowpark application to analyze website traffic data'. You have a DataFrame named 'website_logs' with columns 'user_id', 'page_url', and 'timestamp'. You need to create a new DataFrame that contains the count of distinct users who visited each page within a specific time window Consider the following (incomplete) Snowpark Python code:

Which of the following code lines, when inserted into the Complete the following line...' comment, will correctly calculate the approximate distinct user count for each page within the specified time window?

A. _logs.with_column('distinct_users', F.countDistinct('user_id').over(window_spec)) website
B. website_logs.with_column('distinct_users', F.count('user_id').over(window_spec))
C. website_logs.with_column('distinct_users', F.approx_count_distinct('user_id').over(window_spec))
D. website_logs.groupBy('page_url', F.window('timestamp', '1 hour')).agg(F.countDistinct('user_id').alias('distinct_users'))
E. website_logs.groupBy('page_url').agg(F.countDistinct('user_id').alias('distinct_users'))

Answer: C

Explanation:
The correct code line is 'website_logs.with_column('distinct_users', F.approx_count_distinct('user_id').over(window_spec))'. This uses the function to calculate the approximate distinct count of user IDs within the window defined by 'window_spec' . Option B uses exact count which is less performant. Option A performs an aggregation, which will give a different type of result. Option D uses F.window' which is used for tumbling windows, not sliding windows as requested by the problem.

NEW QUESTION # 149
You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

Answer: A

Explanation:
Option E is the most concise and correct way to define and use the UDF. It uses the '@F.udf decorator, which simplifies the UDF registration process. It also correctly imports 'snowflake.snowpark.functions as F to use the decorator. Options A and D are less efficient because they use 'call_udf instead of directly calling the UDF. Option B registers UDF but does not import required libraries. Option C doesn't import necessary library for the functions.

NEW QUESTION # 150
......

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