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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Java and Scala API basics 2. Environment setup and dependencies
Topic 2: 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 3: Data Transformations and Operations	35%	- DataFrame manipulation 1. Filtering, sorting, grouping, aggregation 2. Joins, unions, set operations 3. Selection, projection, renaming, casting - User-defined logic 1. UDFs, UDAFs, UDTFs 2. Stored procedures with Snowpark - Advanced operations 1. Pivot and unpivot transformations 2. Semi-structured data processing 3. Window functions and analytics
Topic 4: Performance and Best Practices	10%	- Optimization techniques 1. Minimizing data movement 2. Caching and warehouse sizing 3. Query pushdown and execution plans - Security and governance 1. Data protection and compliance 2. Access control and permissions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q23-Q28):

NEW QUESTION # 23
You are developing a Snowpark stored procedure to process PDF files stored in a Snowflake stage. You need to extract text from these PDF files and store the extracted text in a Snowflake table. Due to security requirements, you cannot use any external packages that require internet access. Which of the following approaches can you use to accomplish this task securely and efficiently? (Select all that apply)

A. Use the function to read the PDF files as binary data. Implement a pure-Python PDF parsing library directly within the stored procedure to extract the text. Ensure the library code is included directly in the stored procedure code.
B. Develop a custom Java UDF (User-Defined Function) that uses a secure, open-source PDF parsing library (e.g., PDFBox) and register it with Snowflake. Call this UDF from the Snowpark stored procedure to extract the text.
C. Convert the PDF files to a text-based format (e.g., TXT) using an external tool before loading them into Snowflake. Then, use Snowpark to process the text files.
D. Use Snowpark's built-in PDF parsing functions to extract the text. Snowflake provides native support for PDF parsing, eliminating the need for external libraries.
E. Implement an external function using AWS Lambda or Azure Functions to parse the PDF files and extract the text. Configure the external function to have no internet access.

Answer: A,B

Explanation:
Options B and C are correct. Option B: Java UDFs allow you to leverage existing Java libraries (like PDFBox, which can be included in the UDF's JAR file) to parse PDFs securely within the Snowflake environment. Option C: Using and a pure-Python PDF parsing library (which doesn't require external network access) is another viable approach. The entire library's code must be embedded within the stored procedure. Option A is incorrect because Snowflake does not have built-in PDF parsing functions. Option D is not ideal as you are trying to avoid any external dependencies and internet access. Option E, although workable, adds an external preprocessing step which isn't the most efficient way.

NEW QUESTION # 24
You are developing a Snowpark application that performs feature engineering on a dataset of customer transactions. This involves calculating several complex aggregate features such as rolling averages, medians, and custom ratios. You want to optimize the performance of this feature engineering process using a Snowpark-optimized warehouse. Which of the following strategies would be MOST effective in achieving optimal performance?

A. Implement all feature engineering calculations using Python User-Defined Functions (UDFs) and apply them to the Snowpark DataFrame.
B. Use the 'GROUP BY clause in Snowpark SQL to compute aggregate features, leveraging window functions where appropriate for rolling calculations.
C. Use stored procedures implemented in Java within Snowpark for feature calculations.
D. Materialize intermediate Snowpark DataFrames after each feature engineering step to avoid recomputation.
E. Leverage Snowpark's built-in functions and SQL expressions as much as possible for feature engineering, and rewrite performance-critical calculations as Java or Scala User-Defined Table Functions (UDTFs).

Answer: B,E

Explanation:
Using 'GROUP BY and window functions allows Snowflake to optimize the calculations within its engine. Leveraging UDTFs allows custom computations while still benefiting from Snowflake's optimization capabilities. Python UDFs are generally slower than equivalent SQL or Java/Scala UDTFs due to inter-process communication overhead. Materializing intermediate DataFrames can help in some scenarios but can also introduce overhead if not managed carefully. Java Stored procedures could be used, but UDTF would be more optimized way.

NEW QUESTION # 25
You have a Snowflake table 'raw_events' containing JSON data in a VARIANT column named 'event_data'. This column contains nested JSON objects representing user activity on a website. You need to extract specific nested values and load them into a new Snowflake table 'user_activity' with columns 'event_type' , and 'timestamp'. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this, assuming you want to minimize data transfer and optimize performance? Consider the potential for null values within the JSON.

Answer: C

Explanation:
Option C is the most efficient because it uses Snowflake's native JSON path notation (using 'f) directly within the 'select' statement, which is optimized for Snowflake's internal JSON handling. The other options use function calls ('get' , 'col'), which can introduce overhead. Furthermore, the code uses try_to_timestamp() to avoid errors due to null values

NEW QUESTION # 26
You are optimizing a Snowpark Python application that performs complex data transformations on a large dataset. You notice significant performance bottlenecks. Which of the following optimization techniques would be MOST effective in leveraging the Snowpark architecture to improve performance?

A. Using 'session.sql()' whenever possible instead of Snowpark DataFrame operations.
B. Converting all Snowpark DataFrames to Pandas DataFrames before performing any transformations.
C. Using User-Defined Functions (UDFs) written in Python for all transformations, ensuring they are vectorized where possible, instead of native Snowpark functions.
D. Exploiting lazy evaluation by chaining transformations together and avoiding unnecessary 'collect()' or 'toPandas()' calls.
E. Manually partitioning the DataFrame into smaller chunks before applying transformations.

Answer: D

Explanation:
Lazy evaluation (C) is a key optimization strategy in Snowpark. By chaining transformations, Snowpark can optimize the execution plan and push down operations to the Snowflake engine for efficient processing. Converting to Pandas DataFrames (A) brings data out of Snowflake, negating the benefits of the engine. While vectorized UDFs (B) can be useful, they may not always be as efficient as optimized native Snowpark functions. Manual partitioning (D) is usually handled automatically by Snowflake. session.sql() can bypass optimizations available to Snowpark.

NEW QUESTION # 27
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. Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
B. Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.
C. Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.
D. Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
E. 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.

Answer: B,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 # 28
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