Exam SPS-C01 Cram Review, SPS-C01 Exam Cram Pdf

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q116-Q121):

NEW QUESTION # 116
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?

Answer: A,B

Explanation:
The 'Scaling Up' and 'Scaling Down' thrashing is likely caused by the warehouse suspending too quickly, leading to constant restarts as new requests arrive. Increasing the Auto Suspend time (Option A) prevents this frequent cycling. Increasing the MIN CLUSTER COUNT (Option B) makes more resources readily available, helping the warehouse respond faster to spikes in demand and reducing the need for scaling up. Workload management (Option C) is a good practice but may not directly address the root cause of the instability. Reducing MAX_CLUSTER_COUNT (Option D) could worsen the problem by limiting the warehouse's ability to handle peak loads. Changing to 'ECONOMY' scaling policy (Option E) would prioritize cost over performance, which is counter to improving performance.


NEW QUESTION # 117
Which of the following are key benefits of using Snowpark's server-side execution capabilities for data processing tasks within Snowflake, compared to performing the same tasks on the client-side? (Select all that apply)

Answer: B,C,D,E

Explanation:
Server-side execution in Snowpark offers several advantages: Reduced data transfer: Minimizes the amount of data that needs to be transferred between the client and Snowflake, saving costs and improving performance. Improved security: Keeps sensitive data within Snowflake's secure environment, reducing the risk of data breaches. Increased processing speed: Leverages Snowflake's scalable compute resources, allowing for faster processing of large datasets. Simplified code deployment and maintenance: Stored procedures are deployed and managed within Snowflake, simplifying the deployment and maintenance process. While option E can be true to a degree, it's Python/Scala predominantly, not 'greater' choice. All other options listed here are benefits of server-side execution, the most obvious being reduced data transfer costs, improved security and better scalability.


NEW QUESTION # 118
You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?

Answer: E

Explanation:
Option A is the most efficient. It directly uses the 'filter' method with the combined condition, allowing Snowpark to push down the entire filter to Snowflake for execution. Option B uses 'session.sqr, which might not be as optimized as using the table API directly. Option C pulls the entire table into Pandas, which is highly inefficient for large tables, defeating the purpose of Snowpark. Option D, while functional, is less readable than A, and the multiple 'where' calls are logically equivalent to a single 'filter' with combined conditions. Option E, while functional, contains redundant 'select(' 'V operation.


NEW QUESTION # 119
You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)

Answer: C,D

Explanation:
Options B and D are the most effective. Vectorized operations (B) significantly speed up Pandas calculations. Performing transformations within Snowflake (D) avoids unnecessary data transfer between Snowpark and Pandas, reducing the bottleneck. A is useful, but secondary. C only affects Snowflake side processing, but it may help. E would be useful, but not as helpful as pushing as much as possible down to Snowflake processing.


NEW QUESTION # 120
You are tasked with creating a Snowpark DataFrame from a series of large Parquet files stored in an external stage 'my_stage' . The files contain customer transaction data, but some files are corrupted and cause errors during DataFrame creation. You want to implement a solution that skips the corrupted files and logs the filenames of those files to a table named 'failed_files'. Assuming you have a Snowpark session 'session' and a UDF that inserts filenames into the 'failed_files' table, which of the following approaches is the MOST efficient and robust way to achieve this, while minimizing impact on performance and maintaining data integrity? Consider that you don't have direct control over the file format and data quality within the stage.

Answer: A

Explanation:
Option C is the most efficient and robust. 'COPY INTO with = CONTINUE directly leverages Snowflake's optimized loading capabilities to handle file-level errors gracefully. The 'VALIDATION_MODE allows identifying errored files before the load process. A, B, D and E involve more complex and potentially less efficient workarounds within Snowpark itself.


NEW QUESTION # 121
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