Snowflake Certification SPS-C01

TestPDF 的 Snowflake SPS-C01題庫是由頂級IT專家團隊以最高技術水平整理製作的，確保了試題的準確性和專業性。這些IT團隊成員都是來自指定認證專家、培訓師和 Snowflake 相關工作從業者，他們對 SPS-C01考試內容和 Snowflake Certification 認證要求的資歷瞭如指掌，這樣可以確保 SPS-C01題庫的高質量。

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Snowflake SPS-C01 考試大綱主題:

最新的 Snowflake Certification SPS-C01 免費考試真題：

1. 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)

2. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
B) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
C) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
D) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
E) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.

3. You have a requirement to create Snowpark DataFrames from CSV files located in an AWS S3 external stage. Some CSV files have a header row, while others do not. The files also use different delimiters (comma, semicolon, or tab). You want to create a single function that can handle all these variations, without creating separate functions for each combination. The 'create_dataframe' function receives the stage path, the delimiter, and a boolean indicating whether a header is present. Which of the following code snippets, when implemented inside the function, BEST achieves this goal using the Snowpark Python API? Assume a Snowpark session 'session'.

A)

B)

C)

D)

E)

4. 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) 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) 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.
E) Increase the MIN_CLUSTER_COUNT parameter to pre-warm additional clusters. This ensures that clusters are readily available when the workload increases, reducing latency.

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

問題與答案：