SPS-C01日本語版受験参考書、SPS-C01対応資料

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowpark API for Python	30%	- DataFrame creation and manipulation - Working with Semi-structured data - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management
Topic 2: Performance Optimization and Best Practices	20%	- Caching strategies - Debugging and explain plans - Warehouse sizing for Snowpark - Minimizing data transfer - Vectorized UDFs - Query pushdown and optimization
Topic 3: Data Transformations and DataFrame Operations	35%	- Window functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines - Persisting transformed data - Using built-in functions
Topic 4: Snowpark Concepts	15%	- Snowpark Sessions and connection management - Transformations vs. Actions - Snowpark DataFrames and query plans - Stored procedures and conditional logic - Snowpark architecture and core concepts - Client-side vs. Server-side execution

>> SPS-C01日本語版受験参考書 <<

SPS-C01対応資料 & SPS-C01試験解説

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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題 (Q49-Q54):

質問 # 49
You have a Snowpark DataFrame 'product_df and you want to create a view named 'active_products' that only includes products with a 'status' column value equal to 'ACTIVE'. This view should be accessible to all users in the 'PUBLIC' role. Which of the following code snippets correctly creates the view and grants the necessary privileges?

正解：D

解説：
Option C correctly creates a persistent view ('createOrReplaceView') and then grants the 'SELECT privilege to the 'PUBLIC' role. A temporary view (option B) is session-scoped and wouldn't be accessible to other users or sessions, even with a grant. Option D does not work as Global temporary views created using Snowpark API is not a supported feature..Option E does not apply the filter condition to the View.

質問 # 50
You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A. Install the library directly within the Snowflake Notebook using '!pip install
B. Create a deployment file using setup.py, upload deployment file to stage, and create function
C. Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
D. Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import
E. Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.

正解：E

解説：
Snowflake Notebooks primarily use conda environment specification files ('environment.yml') (B) to manage dependencies. You specify the required libraries in the 'environment.ymr file, upload it to a stage, and use it when creating or updating the Notebook environment. Uploading raw .pV files (A) might work for simple modules, but lacks dependency management. Using '!pip install' (C and E) directly in the notebook is not the intended way to manage dependencies in Snowflake Notebooks for production scenarios and might not persist across sessions.

質問 # 51
A Snowpark application frequently experiences performance degradation during peak hours. You suspect that resource contention on the virtual warehouse is the primary cause. Which of the following actions, when implemented together, would BEST address this issue and ensure consistent performance?

A. Implement workload management rules to prioritize Snowpark workloads during peak hours.
B. Create separate virtual warehouses for different Snowpark workloads and apply resource monitors to limit credit consumption.
C. Enable query acceleration for all Snowpark queries.
D. Convert all Snowpark DataFrames to views to reduce computational overhead.
E. Increase the AUTO_SUSPEND parameter of the virtual warehouse to a shorter duration to release resources more quickly.

正解：A、B

解説：
Workload management rules (A) allow you to prioritize Snowpark workloads, ensuring they receive preferential access to resources during peak hours. Creating separate virtual warehouses (C) isolates different workloads, preventing resource contention and allowing you to apply resource monitors for cost control. Shortening AUTO SUSPEND (B) may lead to frequent warehouse starts, increasing costs. Query acceleration (D) can help, but it might not be the most cost-effective solution for all queries. Converting DataFrames to views (E) doesn't directly address resource contention.

質問 # 52
You have a Snowpark Python application that performs complex data transformations and machine learning model training. The data is stored in Snowflake tables. You notice that model training jobs, specifically those involving large feature sets and iterative algorithms, are consistently slow. The warehouse is already scaled to a LARGE size. Which of the following techniques, when applied individually or in combination, would MOST likely improve the performance of model training in Snowpark?

A. Leverage external functions (IJDFs) to offload computationally intensive operations to specialized hardware outside of Snowflake.
B. Scale the virtual warehouse UP to an XLARGE or larger. This provides more computational resources.
C. Cache intermediate Snowpark DataFrames using to avoid recomputation of common data transformations across multiple training iterations.
D. Use the 'sproc' decorator to define user-defined functions (UDFs) directly within Snowflake, leveraging the platform's optimized execution engine for specific computations.
E. Implement data skipping and filtering strategies to reduce the amount of data read during feature extraction and model training. Pre-aggregate when possible.

正解：C、E

解説：
Caching intermediate DataFrames (Option B) avoids redundant computations, significantly speeding up iterative algorithms. Filtering and data skipping (Option E) reduce the amount of data processed, which is especially beneficial with large feature sets. While sprocs (Option A) offer performance benefits for certain IJDFs, the impact on overall model training performance might be less significant compared to caching and data reduction. External functions (Option C) may introduce network latency, outweighing the potential benefits, unless carefully optimized for data transfer. Scaling up (Option D) might help, but is not the most likely to improve performance, given that the warehouse is already at a LARGE size. Focusing on more efficient data handling is better in most cases.

質問 # 53
You are developing a Snowpark application that processes real-time streaming data'. The application needs to perform a complex calculation for each incoming event. To improve performance, you decide to leverage asynchronous execution and User-Defined Functions (UDFs). However, you are encountering issues with the order of results and ensuring that the processing order matches the arrival order of the events. Which of the following strategies MOST effectively addresses the challenge of maintaining processing order while leveraging asynchronous execution and UDFs in Snowpark?

A. Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.
B. Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.
C. Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.
D. Utilize asynchronous UDF calls with 'block-False' and implement a custom ordering mechanism based on a timestamp or sequence number associated with each event. Store the results in a temporary table and sort them based on the timestamp before further processing.
E. Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.

正解：D

解説：
Asynchronous execution, by definition, doesn't guarantee the order of completion. To maintain order, a custom ordering mechanism based on a timestamp or sequence number is necessary. Storing the results in a temporary table and sorting them before further processing is a viable approach. Synchronous calls (A, D) negate the benefits of asynchronous execution. Snowflake's internal optimization (C) doesn't guarantee order in asynchronous scenarios. Reimplementing the calculation without UDFs (E) might not be feasible or efficient if the calculation is complex. The 'block' parameter is relevant to asynchronous behaviour and must be False' to truly utilize the asynchronous execution model. The UDFs will be executed asynchronously, and the order is maintained by using Timestamp.

質問 # 54
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