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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
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
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
Performance and Best Practices	10%	- Security and governance 1. Access control and permissions 2. Data protection and compliance - Optimization techniques 1. Query pushdown and execution plans 2. Minimizing data movement 3. Caching and warehouse sizing
Data Transformations and Operations	35%	- DataFrame manipulation 1. Selection, projection, renaming, casting 2. Filtering, sorting, grouping, aggregation 3. Joins, unions, set operations - Advanced operations 1. Semi-structured data processing 2. Pivot and unpivot transformations 3. Window functions and analytics - User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q154-Q159):

NEW QUESTION # 154
You are working with a Snowpark DataFrame named 'employees_df' that contains employee data, including a column named 'hire_date' of type String. The date format in the 'hire_date' column is 'MM/DD/YYYY'. You need to convert this column to a DateType so you can perform date-based calculations. Which of the following Snowpark code snippets correctly converts the column to a DateType?

Answer: C

Explanation:
Option C is correct. The 'to_date' function in Snowpark requires the column to be converted as the first argument and the date format as the second argument to correctly parse the string representation of the date. Option A will attempt to convert, but since no format is provided it will fail or produce unexpected results, if the string format matches the default date format. Option B provides the wrong separator. Option D passes column name as string to 'to_date' function, it requires column object and Option E converts to 'TimestampType' , whereas we need 'DateType'.

NEW QUESTION # 155
You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

A. Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.
B. Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
C. Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests
D. Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
E. Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.

Answer: E

Explanation:
Upgrading to a larger warehouse (X-LARGE or higher) provides significantly more CPU resources, which directly addresses the CPU-bound nature of complex JSON parsing and IJDF execution. Scaling out with smaller warehouses (option B) increases concurrency but doesn't necessarily alleviate the CPU bottleneck within individual queries. Materialized Views (option D) can help, but this question is focused on only warehouse sizing options. Option E will almost certainly make things worse. Upgrading to a LARGE could help, but the question asks for the most effective approach. Larger warehouse sizes generally deliver the best performance for intensive workloads.

NEW QUESTION # 156
You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?

Answer: A,D

Explanation:
Options B and E are correct. Option B correctly registers the function 'select_columnS as a stored procedure using "session.sproc.register'. Option E properly constructs the DataFrame by dynamically selecting columns by using 'df[col]'. Option A although syntactically correct may not perform as expected. Option C is incorrect because it attempts to use 'ArrayType' for a standard Python List, which is incompatible. Option D uses columns: str' which makes column as Tuple object instead of List object.

NEW QUESTION # 157
You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

Answer: A

Explanation:
Option C provides the correct way to define and register UDTFs using the class-based approach in Snowpark. It defines the UDTF class with 'process' and methods. returns the schema using 'table' function correctly. Options A and B use the decorator approach, which is valid for simple UDTFs, but it's less flexible than the class-based approach, especially for managing complex state or schema. Option D uses the class-based approach but incorrectly defines the 'output_schema' when registering. Option E has an incorrect definition of return type.

NEW QUESTION # 158
You have a Snowpark DataFrame 'products_df with columns 'product_id', 'category', and 'price'. You want to find the top 3 most expensive products within each category Which of the following Snowpark code snippets will accomplish this, using window functions?

Answer: B

Explanation:
Option D correctly partitions the data by category, orders by price in descending order (most expensive first), assigns a rank using , and then filters for ranks less than or equal to 3. Option A misses the snowflake.snowpark.functions import, but functionally same as D. Option B orders by price in ascending order (cheapest first). Option C does not partition by category and Option E filters where rank < 3 instead of less than or equal to. D is most correct because of syntax and concept implementation, and will pass the code check

NEW QUESTION # 159
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