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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame 'df sales' containing sales data with columns like 'order id', 'product id', 'quantity', and 'sale_price' You want to persist this data into a Snowflake table named "SALES DATA'. You also want to create a dynamic table on top of this base table for faster analytics. You need to choose the appropriate persistence strategy and consider the implications of using a dynamic table. Which of the following options represents the BEST approach?

A) Option C
B) Option A
C) Option E
D) Option B
E) Option D

2. You have a DataFrame 'df containing user profile data'. A column named "profile" stores JSON objects with potentially missing fields. These objects might include 'name', 'age', 'location', and 'preferences'. You need to extract the user's name and age. If 'age' is missing, you want to default to 0. Furthermore, you want to filter out any rows where the 'location' field is an empty string. Which combination of Snowpark code snippets will achieve this? (Select all that apply)

A)

B)

C)

D)

E)

3. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

A)

B)

C)

D)

E)

4. You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

A)

B)

C)

D)

E)

5. A Snowpark application processes streaming data from Kafka, performing complex windowing aggregations. The application is configured with auto-scaling enabled for the virtual warehouse. During peak hours, the application exhibits high latency despite the warehouse scaling up. Upon investigation, you observe sustained high CPU utilization on the single active warehouse. Which actions, alone or in combination, would MOST effectively improve performance while minimizing cost?

A) Decrease the SCALING_POLICY parameter to reduce the time it takes for warehouses to autoscale. This will allow warehouses to keep up with processing as volume increases.
B) Optimize the Snowpark code by using vectorization and efficient data structures. This reduces the CPU load for each processing task.
C) Increase the MAX CLUSTER COUNT parameter for the virtual warehouse. This ensures that the warehouse can scale out to a greater number of clusters to handle the increased workload.
D) Increase the MIN_CLUSTER_COUNT parameter to pre-warm additional clusters. This ensures that clusters are readily available when the workload increases, reducing latency.
E) Repartition the input data to distribute the workload more evenly across the available clusters. Ensure the partitioning key is suitable for the aggregations being performed.

Solutions: