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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance and Best Practices | 10% | - Optimization techniques
|
| Snowpark Concepts and Architecture | 25% | - Session management and connection
|
| Data Transformations and Operations | 35% | - Advanced operations
|
| Snowpark API and Development | 30% | - Python API fundamentals
|
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
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. Increase the MIN_CLUSTER_COUNT parameter to pre-warm additional clusters. This ensures that clusters are readily available when the workload increases, reducing latency.
- B. Optimize the Snowpark code by using vectorization and efficient data structures. This reduces the CPU load for each processing task.
- C. 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.
- D. 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.
- E. 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.
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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 D
- B. Option A
- C. Option B
- D. Option C
- E. Option E
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
You are developing a Snowpark application that needs to connect to Snowflake using account identifiers. Your organization's Snowflake account is configured with federated authentication (Okta). Which of the following methods is the most secure and recommended way to establish a Snowpark session in this scenario, avoiding hardcoding credentials in your application and leveraging existing authentication mechanisms?
- A. Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
- B. Pass username and password directly in the connection properties along with the account identifier.
- C. Utilize Snowflake's support for OAuth and configure your application to acquire a token from Okta and use it to establish the Snowpark session using the 'authenticator parameter set to 'oauth'.
- D. Use the connection parameter along with username and password directly in the connection properties.
- E. Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
Consider a JSON structure representing product information, where prices are stored as strings due to inconsistent data quality. You need to calculate the average price of products. However, some price strings contain non-numeric characters (e.g., '$', commas). Which of the following approaches, using Snowpark DataFrame operations, is the MOST robust and efficient way to clean and cast the price data to a numeric type for accurate average calculation?
- A.
- B.
- C.
- D.
- E.
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
You have a Snowpark DataFrame named with the following schema: '(timestamp: TmestampType, sensor_id: StringType, value: FloatType)'. You need to identify the top 3 sensors with the highest average value over the entire dataset. Which of the following Snowpark Python code snippets correctly implements this requirement?
- A.
- B.
- C.
- D.
- E.
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
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