使用SPS-C01考題資訊 -告別Snowflake Certified SnowPro Specialty - Snowpark考試煩惱
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SPS-C01考題資訊 - 您最聰明的選擇Snowflake Certified SnowPro Specialty - Snowpark考試資料
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最新的 Snowflake Certification SPS-C01 免費考試真題 (Q58-Q63):
問題 #58
You are developing a Snowpark stored procedure to process PDF files stored in a Snowflake stage. You need to extract text from these PDF files and store the extracted text in a Snowflake table. Due to security requirements, you cannot use any external packages that require internet access. Which of the following approaches can you use to accomplish this task securely and efficiently? (Select all that apply)
- A. Develop a custom Java UDF (User-Defined Function) that uses a secure, open-source PDF parsing library (e.g., PDFBox) and register it with Snowflake. Call this UDF from the Snowpark stored procedure to extract the text.
- B. Use Snowpark's built-in PDF parsing functions to extract the text. Snowflake provides native support for PDF parsing, eliminating the need for external libraries.
- C. Use the function to read the PDF files as binary data. Implement a pure-Python PDF parsing library directly within the stored procedure to extract the text. Ensure the library code is included directly in the stored procedure code.
- D. Implement an external function using AWS Lambda or Azure Functions to parse the PDF files and extract the text. Configure the external function to have no internet access.
- E. Convert the PDF files to a text-based format (e.g., TXT) using an external tool before loading them into Snowflake. Then, use Snowpark to process the text files.
答案:A,C
解題說明:
Options B and C are correct. Option B: Java UDFs allow you to leverage existing Java libraries (like PDFBox, which can be included in the UDF's JAR file) to parse PDFs securely within the Snowflake environment. Option C: Using and a pure-Python PDF parsing library (which doesn't require external network access) is another viable approach. The entire library's code must be embedded within the stored procedure. Option A is incorrect because Snowflake does not have built-in PDF parsing functions. Option D is not ideal as you are trying to avoid any external dependencies and internet access. Option E, although workable, adds an external preprocessing step which isn't the most efficient way.
問題 #59
You have written a Snowpark Python function that performs a complex calculation involving user-defined functions (UDFs). When running this function on a large dataset, you encounter a 'PicklingError: Can't pickle ': it's not the same object as main.my function'. What is the MOST likely cause of this error, and how can you resolve it?
- A. The dataset is too large to be processed in memory. Use 'df.cache()' to persist the intermediate results to disk.
- B. The UDF contains unsupported Python libraries. Ensure that all dependencies are available on the Snowflake worker nodes.
- C. The UDF's return type is not correctly specified. Use 'udf(func, to explicitly define the return type.
- D. The UDF is defined within a local scope or closure, and Snowpark cannot serialize it. Move the UDF definition to the global scope or use 'cloudpickle' explicitly.
- E. The Snowpark session is not properly initialized. Ensure that the connection parameters are correct.
答案:D
解題說明:
Pickling errors in Snowpark often arise when UDFs are defined within local scopes because the serialization process needs to transmit the function to the Snowflake worker nodes. Moving the UDF to the global scope or using 'cloudpickle' allows the function to be correctly serialized. Option B addresses memory issues, C handles dependency problems, D addresses connection issues, and E addresses return type issues, but these are not the MOST likely cause of a PicklingError related to function scope.
問題 #60
You have a Snowpark application that processes sensitive data'. To enhance security, you want to use key pair authentication and ensure that the private key is never exposed in plain text within the application or logs. Which of the following strategies offers the most robust protection against accidental key exposure, even in the event of a security breach of the application server itself?
- A. Use a dedicated Hardware Security Module (HSM) or a cloud-based key management service (e.g., AWS KMS, Azure Key Vault) to store and manage the private key. Configure the Snowpark application to retrieve the key only when needed and never store it locally.
- B. Store the private key in an environment variable on the application server, ensuring that only the application user has read access.
- C. Store the private key encrypted using AES encryption with a key derived from a passphrase. Prompt the user for the passphrase each time the application starts.
- D. Store the private key in an encrypted file with restricted access, decrypting it only during session creation and overwriting the memory location immediately afterwards.
- E. Store the private key encrypted with a symmetric key. Store the symmetric key in a separate environment variable.
答案:A
解題說明:
Using a dedicated HSM or cloud-based key management service (C) offers the strongest protection. HSMs and KMSs are specifically designed to securely store and manage cryptographic keys, providing hardware-backed security and access controls. The private key never leaves the HSM/KMS, minimizing the risk of exposure. Option A, storing in an environment variable, provides some protection but is still vulnerable if the server is compromised. Option B simply moves the problem of key management from one key to another. The symmetric key is also vulnerable. Option D requires user interaction and the passphrase could be compromised. Option E improves on environment variables, but is still less secure than HSM and KMS. HSMs/KMSs are FIPS 140-2 Level 3 validated, which is most secure.
問題 #61
A Snowpark Python application is failing intermittently with a 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: Remote service internal error [Errorld: ...l' when calling 'df.collect()' on a DataFrame that results from joining multiple tables and applying a complex filter. The data volume is substantial, but within the warehouse's expected capacity. Which of the following actions are MOST likely to resolve this issue? (Select two)
- A. Implement retry logic around the 'df.collect()' call with exponential backoff, assuming the error is transient due to resource contention.
- B. Break down the complex query into smaller, intermediate DataFrames and persist them using to avoid memory pressure during a single large query.
- C. Increase the parameter to a higher value to prevent session timeouts.
- D. Replace with 'df.toPandas(Y to improve memory management on the client side.
- E. Switch to using the function with a raw SQL query instead of Snowpark DataFrame operations.
答案:A,B
解題說明:
Options B and C are the most likely to resolve the issue. Option B addresses potential memory pressure within Snowflake by breaking down the query and persisting intermediate results. Option C acknowledges that the error might be transient due to resource contention and implements retry logic. Increasing (A) is unlikely to solve a remote service internal error. 'df.toPandas()' (D) might exacerbate the problem by moving more data to the client. Using (E) is a workaround, but doesn't address the underlying problem within Snowpark and could reduce performance if not carefully optimized.
問題 #62
You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?
- A.

- B.

- C.

- D.

- E.

答案:E
解題說明:
Option C provides the correct way to define and register UDTFs using the class-based approach in Snowpark. It defines the UDTF class with 'process' and methods. returns the schema using 'table' function correctly. Options A and B use the decorator approach, which is valid for simple UDTFs, but it's less flexible than the class-based approach, especially for managing complex state or schema. Option D uses the class-based approach but incorrectly defines the 'output_schema' when registering. Option E has an incorrect definition of return type.
問題 #63
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