Snowflake SPS-C01 valid study dumps : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Snowpark Developer Certification Exam
Exam Number:	SPS-C01
Exam Format:	Multiple select, Multiple choice
Exam Price:	$375 USD
Related Certifications:	SnowPro Core Certification
Real Exam Qty:	60-70
Available Languages:	English
Exam Duration:	115 minutes
Certificate Validity Period:	2 years
Recommended Training:	Snowpark Documentation Snowflake University Snowpark Courses
Exam Registration:	Snowflake Training & Registration Snowflake Certification Portal
Sample Questions:
Exam Way:	Online proctored exam
Pre Condition:	Recommended: SnowPro Core Certification and practical experience with Snowflake and Python.
Official Syllabus URL:	https://www.snowflake.com/training/certification/

Snowflake SPS-C01 Exam Syllabus Topics:

SPS-C01 (Snowflake) Exam: Your Questions Answered

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a Snowpark DataFrame with columns 'order_id', 'product_id', 'sale_date' (DATE), and 'sale_amount'. You need to perform the following transformations: 1. Filter out sales records before January 1, 2023.2. Group the data by 'product_id' and calculate the total 'sale_amount' for each product. 3. Create a new column 'average_sale_amount' by dividing the total 'sale_amount' by the number of distinct 'order_id' for each product. You must alias the aggregate function. Which of the following Snowpark code snippets correctly implements these transformations?

A.

B.

C.

D.

E.

Question 2

You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

A. Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
B. Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
C. Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.
D. Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.
E. Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.

Question 3

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. Convert the DataFrame to a Pandas DataFrame before applying the UDF.
B. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
C. Ensure that the data is pre-sorted according to the primary key of the table 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.

Question 4

You are tasked with creating a Snowpark DataFrame from a series of large Parquet files stored in an external stage 'my_stage' . The files contain customer transaction data, but some files are corrupted and cause errors during DataFrame creation. You want to implement a solution that skips the corrupted files and logs the filenames of those files to a table named 'failed_files'. Assuming you have a Snowpark session 'session' and a UDF that inserts filenames into the 'failed_files' table, which of the following approaches is the MOST efficient and robust way to achieve this, while minimizing impact on performance and maintaining data integrity? Consider that you don't have direct control over the file format and data quality within the stage.

A. Use 'session.read.parquet('stage://my_stage/ within a try-except block to catch errors. Inside the 'except' block, call with the filename. Retry the read operation for remaining files after removing the failing file from stage.
B. Implement a custom file listing function using 'session.sql('LlST to identify potentially corrupted files by checking file size or metadata, then exclude these files when creating the Snowpark DataFrame. Use 'session.read.parquet' with the filtered list of files.
C. Use the command in Snowflake to load the Parquet files into a temporary table, specifying the 'ON ERROR = CONTINUE option. Then, create a Snowpark DataFrame from the temporary table. Log any rejected files using a 'VALIDATION MODE = RETURN ERRORS' copy command before creating the temporary table.
D. Create a Snowpark DataFrame using 'session.read.option('mode', 'PERMISSIVE').parquet('stage://my_stage/ ')'. This automatically skips corrupted records within valid files but doesn't handle entire corrupted files. Afterward, compare the counts of each file before and after processing to identify corrupted files based on lost records.

Question 5

You are developing a Snowpark application that utilizes a UDF. You need to ensure that the UDF runs with the privileges of the caller (the user executing the query). Which of the following steps are necessary to accomplish this while creating the Snowpark session?

A. When creating the Snowpark session, explicitly set the 'privilege' parameter to 'CALLER.
B. After creating the Snowpark session, execute the SQL command 'ALTER SESSION SET
C. The account administrator needs to explicitly grant the 'CREATE FUNCTION' privilege to the user.
D. When defining the UDF using Snowpark, ensure the argument is passed in the decorator. Create the Snowpark session as usual.
E. No special steps are required when creating the Snowpark session; the UDF automatically inherits the caller's privileges.

Solutions: