Quiz Snowflake - SPS-C01–High Pass-Rate Testking Learning Materials

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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowpark APIs and supported languages 2. Snowflake execution model overview
DataFrame Operations and Data Processing	- Data transformation workflows 1. Joins and window functions 2. Filtering, selecting, and aggregations
Data Engineering with Snowpark	- Pipeline development 1. Batch processing workflows 2. Integration with Snowflake data pipelines
Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Resource utilization tuning 2. Pushdown optimization concepts
Testing, Debugging, and Deployment	- Production readiness 1. Debugging Snowpark applications 2. Deployment strategies
User Defined Functions and Stored Procedures	- Extending Snowpark with custom logic 1. Python UDFs 2. Stored procedures in Snowpark

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

NEW QUESTION # 145
Consider the following Snowpark Python code snippet designed to aggregate sales data by region:

During testing, you observe that the performance of this code is suboptimal, especially when dealing with very large 'SALES DATA tables. Using Snowflake's query history, you notice that a significant amount of time is spent on data shuffling during the operation. What optimization strategies could you employ within this Snowpark code to minimize data shuffling and improve the overall performance?

A. Apply a more selective 'filter operation to the 'sales_df' DataFrame before the 'groupBy' operation, reducing the amount of data that needs to be shuffled.
B. Repartition the 'sales_df DataFrame using 'repartitionBy' before the 'groupBy' operation, specifying the 'REGION' column to ensure that data for each region is co-located on the same node.
C. Utilize the 'hint' function in Snowpark to provide a join hint that suggests a specific join strategy to the Snowflake query optimizer.
D. store 'SALES_DATX as a clustered table using the 'REGION' column.
E. Increase the warehouse size to provide more resources for data processing, which will inherently reduce data shuffling.

Answer: A,B,D

Explanation:
Filtering early reduces data shuffling by reducing the volume of data that needs to be grouped. Repartitioning ensures that rows with the same region are on the same node before the group by which reduces shuffling significantly. Creating the source SALES_DATA table as a clustered table with REGION as the clustering key colocates the data for each region in the same micropartitions, reducing shuffling. Increasing warehouse size will help overall performance but will not directly reduce shuffling. Hints apply to Joins, not group by operations.

NEW QUESTION # 146
A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

A. Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.
B. Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.
C. Set the 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.
D. Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.
E. Generate an RSA key pair using 'ssh-keygen' and store the private key securely.

Answer: D,E

Explanation:
Key Pair Authentication requires generating an RSA key pair (Option A) and configuring the Snowflake user with the public key (Option D). Setting the 'AUTHENTICATOR parameter to 'snowflake' is the correct approach. 'EXTERNALBROWSER uses the web-based browser authentication, while the private key is specified within the connection parameters directly, not the Snowflake CLI config.

NEW QUESTION # 147
Consider a Snowpark DataFrame with a containing date values, some of which are corrupted (e.g., invalid date formats or out-of-range values). You need to identify and either remove or correct these corrupted date values. Which of the following approaches can be effectively used in Snowpark Python to handle such scenarios? (Select all that apply)

A. Use the 'to_date' function with the 'IGNORE' keyword to automatically replace invalid dates with NULL.
B. Directly replace corrupt dates using df.na.fill() after identifying the corrupt dates.
C. Use function to convert the column and filter out rows where the conversion results in NULL.
D. Implement a regular expression-based filter to remove rows where the 'date_column' does not match a valid date format.
E. Define a UDF that attempts to parse the date string and returns a valid date or None if parsing fails; then, use this UDF to create a new column and filter rows with None values.

Answer: C,D,E

Explanation:
Options A, B, and D are valid approaches. Option A allows converting the column to date, setting incorrect values as NULL, which can then be filtered. Option B (UDF) provides flexibility for custom date parsing and error handling. Option D is valid as well, as it filters out data which doesn't match the expression. Option C is incorrect. The 'to_date' function in Snowpark does not have an 'IGNORE' keyword to replace invalid dates with NULL automatically. If it fails to convert to date, it will throw an error. Option E assumes that the corrupted dates can be pre-identified, which is generally not the case as the process aims to identify the dates that are corrupt, thus making it an incorrect answer.

NEW QUESTION # 148
A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

Answer: D

Explanation:
Option A, , is the most efficient and directly creates a temporary view scoped to the session. The 'createOrRepIaceTempView' method is the correct Snowpark API call for creating such views. Options B and D, 'createOrReplaceView' with or 'createView' with , are not valid Snowpark API calls. Option C, createOrReplaceGlobalTempViews , creates a global temporary view, accessible across sessions, which is not the requirement. Option E is adding a redundant step by creating a dataframe from another dataframe.

NEW QUESTION # 149
You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
B. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
C. Convert the DataFrame to a Pandas DataFrame before applying the UDF.
D. Broadcast the DataFrame to all compute nodes before applying the UDF.
E. Increase the number of UDF worker threads within the UDF definition.

Answer: B

Explanation:
Repartitioning the DataFrame using allows you to control how the data is distributed across compute nodes. This can improve performance by ensuring that related data is processed together, reducing data shuffling and improving data locality. Pre- sorting data (A) might help in some cases, but it doesn't guarantee optimal data distribution for parallel processing. Broadcasting the DataFrame (C) is suitable for smaller datasets, not large ones where it can lead to memory issues. Converting the DataFrame to a Pandas DataFrame (D) defeats the purpose of using Snowpark for distributed processing and introduces a single-node bottleneck. There's no direct control over the number of UDF worker threads in Snowflake.

NEW QUESTION # 150
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