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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineering team has developed a Snowpark Python application to process customer orders, enrich them with external data (e.g., geo location, weather) and update the Customer360 table. The application is deployed to a production environment. The application's latency has significantly increased over the last week. Your investigation reveals that the Snowflake warehouse used by the application is constantly switching between the 'Scaling Up' and 'Scaling Down' states. The team has set the Auto Suspend time to 5 minutes and Auto Resume to True. Assuming that the team hasn't changed the code, the external API or any parameter related to data ingestion, which combination of the following actions would MOST likely fix the warehouse instability issue and improve the performance of this Snowpark application in production without substantial cost increases?

A) Change the scaling policy of the warehouse to 'ECONOMY', prioritizing cost efficiency over performance responsiveness.
B) Implement workload management and classification to ensure the Customer360 updates are prioritized over less important tasks and assigned to a dedicated resource pool.
C) Increase the Auto Suspend value from 5 minutes to 30 minutes. This will ensure that the warehouse remains active for a longer period, preventing frequent auto- suspends and subsequent resume operations.
D) Reduce the MAX CLUSTER COIJNT to limit the potential peak capacity of the warehouse, preventing excessive resource allocation.
E) Increase the MIN_CLUSTER_COUNT of the warehouse. This pre-warms clusters and helps the warehouse to quickly adjust to workload changes.

2. 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) Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
B) Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.
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) Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.
E) Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.

3. You're using Snowpark in Python and need to execute a complex SQL query. The query involves several joins and aggregations, and you want to optimize its performance. You are using "session.sql(query)' to execute the query. Which of the following strategies, applied before executing 'session.sql(query)' , would likely lead to the most significant performance improvement for a very large dataset?

A) Use the method on the DataFrame returned by 'session.sql(queryy.
B) Ensure that the SQL query includes appropriate comments to improve readability.
C) Convert the SQL query into a series of Snowpark DataFrame operations (e.g., 'groupBy()', 'agg()').
D) Create a view of the underlying data source instead of directly querying the table.
E) Reduce the size of the data by filtering the DataFrame returned by 'session.sql(queryy using 'where()' before executing any further operations.

4. You have a Snowpark Python stored procedure named 'calculate_stats' that takes a table name as input and returns summary statistics. You need to modify the stored procedure to add a new optional parameter for specifying a filter condition. Which of the following SQL commands, used in conjunction with the Snowpark API for Python, is the MOST efficient way to alter the existing stored procedure without dropping and recreating it?

A)

B)

C)

D)

E)

5. You have JSON files stored in an internal stage named 'json_stage' within your Snowflake account. Each JSON file contains an array of product objects, with potentially nested structures. You need to create a Snowpark DataFrame to analyze this data, but the schema is complex and you want to avoid explicitly defining it in your Python code. Which of the following Snowpark code snippets will MOST effectively achieve this, assuming you have a Snowpark session object named 'session'?

A)

B)

C)

D)

E)

Solutions: