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Snowflake SPS-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q342-Q347):

NEW QUESTION # 342
You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?

A. After the merge, query the 'sales_table' and compare it to a copy of the 'sales_table' taken before the merge. Identify inserted rows as those present in the new version but not in the old, and updated rows as those with different values in specific columns between the two versions. This strategy should be implemented with the help of external functions and UDFs, as it is an expensive operation and should not be computed with normal SQL.
B. The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.
C. It's not possible to capture the merge operation details (inserted/updated/unchanged rows) directly using Snowpark alone. You would need to implement custom logic outside of Snowpark to compare the data before and after the merge.
D. You can use the 'returning' clause within the merge statement to retrieve the impacted rows and their change status (INSERTED, UPDATED) and then write this data to a tracking table using 'write.saveAsTable' .
E. You can combine the 'returning' clause along with a stored procedure to retrieve the inserted/updated rows with their status and then use the stored procedure to write to a different tracking table using insert statement.

Answer: D

Explanation:
The 'returning' clause is a powerful feature of the 'merge' statement in Snowflake SQL. It allows you to capture the rows that were affected by the merge operation, along with details about the type of change (INSERTED, UPDATED, DELETED). In Snowpark, you can leverage this by including a 'returning' clause in your 'merge' statement and then use the returned DataFrame to write the data to a tracking table. This provides a direct and efficient way to monitor the impact of your merge operations. Therefore the correct answer is B.

NEW QUESTION # 343
Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A. Use to explicitly declare the Pandas dependency.
B. Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.
C. Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.
D. Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.
E. Change 'FloatType' to 'DoubleType' as it provides more precision.

Answer: B,C,D

Explanation:
Clustering by 'ticker_symbol' significantly optimizes the 'groupBy' operation by organizing data physically for efficient retrieval. Caching the result before writing to a table is crucial if the intermediate result 'result' will be used multiple times, avoiding redundant computations. Repartitioning by 'ticker_symbol' before grouping helps distribute data evenly, improving parallelism and locality. While Pandas is generally available, explicitly declaring it might be necessary in certain isolated environments, so B can be considered a good practice, but not as impactful. Changing FloatType to DoubleType (C) will not lead to performnace improvement. It only changes precision level which is not required here.

NEW QUESTION # 344
You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

Answer: C

Explanation:
Option D is the correct solution. First, 'customer_id')' renames the 'id' column in 'df2 to 'customer_id', aligning it with the 'customer_id' column in 'dfl Then, 'cifl performs the set difference operation, returning only the 'customer_id' values present in 'dfl& but not in the modified 'df2. 'exceptAll' (Option A) will include duplicates. Option B uses 'minus' which does not exist on Snowpark DataFrame. Options C uses 'subtract which also does not exist. Option E will cause unexpected results because the column name of dfl and df2 would be different.

NEW QUESTION # 345
You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?

Answer: A,B

Explanation:
The correct answers are B and E. B: This option correctly loads an encrypted private key from a file using the 'cryptography' library, specifying the passphrase and backend. E: Option E directly utilizes snowflake.connector.read_private_key functionality simplifying the process if using snowflake-connector. Option A fails to specify the backend for the serialization, which is necessary for certain key types and may lead to errors. Option C simply reads the key as a string without decryption. Option D assumes the private key is stored in an environment variable, which does not include any decryption.

NEW QUESTION # 346
You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

A. Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).
B. Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =
C. Using the method to replace null values in the 'description' column with a default string 'No description available'.
D. Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.
E. Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.

Answer: A,C,E

Explanation:
Options A, B, and D can be efficiently chained using Snowpark DataFrame operations. Option A Cna.fill()') is a built-in method for handling null values. Option B is a SQL function available in Snowpark for string manipulation. Option D ('coalesce()') effectively fills null values from another column if present. Option C, using a UDF for string standardization, is viable but potentially less efficient than using built-in functions if possible. Option E is extremely inefficient as it forces data transfer to the client and row-by-row processing instead of leveraging Snowflake's parallel processing capabilities. Chaining operations allows Snowpark to optimize the execution plan and potentially perform these transformations in a single pass over the data. UDF execution might introduce overhead.

NEW QUESTION # 347
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