Study with SPS-C01 most valid questions & verified answers

Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:

A)

B)

C)

D)

E)

2. You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

A) Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).
B) Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
C) Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
D) Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.
E) Disable automatic query optimization features in Snowflake to gain more control over query execution.

3. A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

A) Use Snowflake Pipes to ingest data and trigger the stored procedure based on new data arrival.
B) Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.
C) Create a Python script that uses the Snowpark API to connect to Snowflake and execute the stored procedure, then schedule the script using a Linux cron job.
D) Utilize a third-party orchestration tool, such as Airflow, to schedule and monitor the execution of the stored procedure through the Snowflake connector.
E) Implement a Streamlit application that calls the stored procedure when a button is pressed.

4. You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

A)

B)

C)

D)

E)

5. You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

A) Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.
B) Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
C) Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
D) Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.
E) Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.

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