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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

A) Reduce the 'window_size' parameter. Smaller windows inherently require less computation.
B) Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.
C) Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.
D) Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.
E) Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.

3. You are developing a Snowpark application that performs several complex transformations on a large DataFrame representing customer purchase history. This DataFrame is used multiple times in the application. You need to optimize the application's performance by caching the DataFrame. Which of the following approaches is the MOST efficient and memory-conscious way to cache the DataFrame in Snowpark?

A) Using to store the entire DataFrame in a Python list, then creating a new DataFrame from that list for each subsequent operation.
B) Creating a temporary table in Snowflake using and then reading it back into a new DataFrame for each subsequent operation.
C) Using immediately after the initial DataFrame creation.
D) Using 'session.createDataFrame(df.toPandas())' to convert the Snowpark DataFrame to Pandas and back to Snowpark DataFrame.
E) Using without specifying a storage level. Snowflake will choose a default storage level.

4. A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?

A) Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.
B) Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.
C) Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.
D) Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
E) Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.

5. You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A) Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.
B) Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.
C) Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.
D) Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.
E) Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.

Solutions: