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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Performance Optimization and Best Practices	- Efficient Snowpark execution 1. Resource utilization tuning 2. Pushdown optimization concepts
Topic 2: Snowpark Fundamentals	- Snowpark architecture and concepts 1. Snowflake execution model overview 2. Snowpark APIs and supported languages
Topic 3: Data Engineering with Snowpark	- Pipeline development 1. Batch processing workflows 2. Integration with Snowflake data pipelines
Topic 4: Testing, Debugging, and Deployment	- Production readiness 1. Debugging Snowpark applications 2. Deployment strategies
Topic 5: DataFrame Operations and Data Processing	- Data transformation workflows 1. Filtering, selecting, and aggregations 2. Joins and window functions
Topic 6: 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 (Q108-Q113):

NEW QUESTION # 108
A data engineer is tasked with calculating a 3-month rolling average of sales data using Snowpark Python. The sales data is stored in a table named 'SALES DATA' with columns 'sale_date' (DATE) and (NUMBER). They need to use a table function to accomplish this efficiently. Which of the following Snowpark Python code snippets correctly implements this rolling average calculation using a table function?

Answer: C

Explanation:
Option E correctly defines a table function that takes a DataFrame as input and returns a DataFrame with the rolling average calculated using the 'avg()' function and a window specification. It then uses 'session.table_function' to apply the table function to the DataFrame and selects the required columns. Option A attempts to call directly within the table function definition which is invalid. Option B passes 'session' as the first argument to table function that's wrong. Option C defines that aren't compatible with DataFrame operations, this approach is not how you should do. Option D is same as C, and incorrect because, it attempts to use window functions inside the table function where the input is a table function input types.

NEW QUESTION # 109
You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)

A. Use 'df.write.option('compression', inside a 'try-except block.
B. Configure the stage 'my_stage' with FILE_FORMAT = (TYPE = 'JSON', COMPRESSION = 'SNAPPY') and then use within a 'try-except' block.
C. Wrap the entire write operation in a try-except block and implement retry logic with exponential backoff in case of transient errors.
D. Define a UDF to write the dataframe into stage along with exception handling logic.
E. Use compression='snappy', mode='overwrite')' and handle potential exceptions using a 'try-except' block.

Answer: A,C,E

Explanation:
Option A directly utilizes the .json(V method for writing JSON files with snappy compression, specifying the 'overwrite' mode, and the 'try-except block adds error handling. Option C specifies the format to JSON using format('json') with snappy compression and specifying the 'overwrite' mode, and using a 'try-except' block for error handling. Option E deals with write errors by wrapping the entire process inside try-except block. Options D is not correct since creating a UDF just for this purpose introduces unnecessary overhead compared to the simple and direct approaches provided by A and C. Option B is incorrect, while configuring the stage with a default file format is useful, it won't apply in this specific write operation using DataFrameWriter. We need to explicitly define format and compression for the operation.

NEW QUESTION # 110
You're building a Snowpark Python application that processes sensor data from various devices. The data arrives as a stream of JSON objects, each containing the device ID, timestamp, and sensor readings. You want to use a Streamlit application to visualize near real- time aggregates on the data'. You're aiming to create a Snowpark DataFrame from this data, perform transformations, and then serve this DataFrame to Streamlit. Which of the following approaches concerning creating the initial DataFrame from JSON data is generally the MOST efficient and scalable for handling such a stream of data?

A. Read the JSON data directly from the stream into a Pandas DataFrame using , then convert the Pandas DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'.
B. Write each incoming JSON object to a temporary file in cloud storage (e.g., AWS S3 or Azure Blob Storage) and then periodically use 'session.read.json()' to create a Snowpark DataFrame from the files.
C. Use Snowflake's Kafka connector to ingest the JSON data directly into a Snowflake table, and then create a Snowpark DataFrame from that table using 'session.table()'.
D. Utilize Snowpipe with auto-ingest configured to load the JSON data into a raw data Snowflake table, and subsequently, establish a Snowpark DataFrame using 'session.table('raw_data_table')'. You can then apply necessary transformations using Snowpark.
E. Iteratively append each JSON object to a Python list, then create a Snowpark DataFrame from the list using 'session.createDataFrame(list_of_json_objectsy.

Answer: C

Explanation:
Using Snowflake's Kafka connector (or a similar streaming ingestion service) is the most efficient and scalable way to handle streaming data. It allows for near real-time ingestion and avoids intermediate steps like writing to temporary files or using Pandas DataFrames. Using Snowpipe with auto-ingest is also a valid approach, however Kafka connector is slightly better suited for streaming data because of its real time data processing. Kafka is also a common real time streaming platform. Therefore, option C is the best answer. Other options may encounter scalability and performance issues with high-volume, continuous data streams.

NEW QUESTION # 111
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. 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.
B. 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.
C. 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' .
D. 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.
E. The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.

Answer: C

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 # 112
You have a Snowpark DataFrame 'customer data df containing customer information, including 'customer id', 'email', and phone number'. You need to anonymize the 'email' and 'phone number" columns for customers residing in specific countries (e.g., 'USA', 'Canada') before persisting the changes back to the 'customers' table. Anonymization should replace sensitive data with 'XXXXX'. You want to leverage UDF for obfuscation. What is correct and optimal approach considering performance and security?

A. Option C
B. Option D
C. Option B
D. Option A
E. Option E

Answer: C,E

Explanation:
Options B and E provide the most efficient and secure solutions. Option B correctly utilizes the 'when' function along with a registered UDF ('anonymize_udf) to conditionally anonymize the 'email' and 'phone_number' columns only for customers in 'USA' or 'Canada'. This ensures that only the necessary data is modified. Option E creates and uses a temporary UDF with SQL, achieving the same result in a different (yet valid) way. Option A is less efficient because it filters the DataFrame, updates it, and then joins it back with the original, which can be computationally expensive. Additionally, join can produce cartesian results if not done properly. Option C, while straightforward, hardcodes the anonymized value directly, which might not be the best practice in all scenarios and doesn't use a UDF. Option D, although similar to B, attempts to create a permanent UDF without specifying the stage location, which may lead to errors and is not best practice unless you manage it. Also creating a UDF in Python using SQL can be faster and safer.

NEW QUESTION # 113
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