最新的Snowflake Certification認證 SPS-C01考試題庫

問題1

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. Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.

B. Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.

C. Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.

D. Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.

E. Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.

正確答案: B

說明：（僅 VCESoft 成員可見）

問題2

A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

A.

B.

C.

D.

E.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題3

You have configured your Snowpark application to use a '.env' file for storing connection parameters. The ' .env' file contains the following:

Which of the following code snippets demonstrates the most secure and recommended method for creating a Snowpark session using these environment variables and also ensuring the file exists?

A.

B.

C.

D.

E.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題4

Which of the following statements are correct regarding the authentication methods available when creating a Snowpark session?

A. Key pair authentication uses a private key to establish a secure connection without needing to store passwords directly in the code.

B. OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.

C. OAuth authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.

D. Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.

E. Using username/password authentication is the most secure and recommended approach for production environments.

正確答案: A,C,D

說明：（僅 VCESoft 成員可見）

問題5

You're building a Snowpark Python application that processes sensor data from various devices. The data arrives as a stream of JSON objects, each containing the device ID, timestamp, and sensor readings. You want to use a Streamlit application to visualize near real- time aggregates on the data'. You're aiming to create a Snowpark DataFrame from this data, perform transformations, and then serve this DataFrame to Streamlit. Which of the following approaches concerning creating the initial DataFrame from JSON data is generally the MOST efficient and scalable for handling such a stream of data?

A. Read the JSON data directly from the stream into a Pandas DataFrame using , then convert the Pandas DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'.

B. Write each incoming JSON object to a temporary file in cloud storage (e.g., AWS S3 or Azure Blob Storage) and then periodically use 'session.read.json()' to create a Snowpark DataFrame from the files.

C. Utilize Snowpipe with auto-ingest configured to load the JSON data into a raw data Snowflake table, and subsequently, establish a Snowpark DataFrame using 'session.table('raw_data_table')'. You can then apply necessary transformations using Snowpark.

D. Use Snowflake's Kafka connector to ingest the JSON data directly into a Snowflake table, and then create a Snowpark DataFrame from that table using 'session.table()'.

E. Iteratively append each JSON object to a Python list, then create a Snowpark DataFrame from the list using 'session.createDataFrame(list_of_json_objectsy.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題6

You are developing a Snowpark application that involves creating a set of stored procedures and UDFs to process data'. To ensure proper version control and dependency management, you decide to package your Python code into a single Python Wheel file and deploy it to Snowflake. Which of the following methods are valid for deploying and utilizing this Python Wheel file within Snowflake, considering best practices for maintainability and security? (Select TWO)

A. Upload the Python Wheel file to an internal stage and directly reference it in the UDF or stored procedure definition using the 'USING' clause.

B. Use the Snowsight UI to upload the Python Wheel file as a dependency for the Snowflake environment, making it available for all stored procedures and UDFs within that environment.

C. Create a Conda environment file (environment.yml) that specifies the Python Wheel file as a dependency and use this file to create a Snowflake environment. Then, associate the stored procedures and UDFs with that environment.

D. Use the 'snowflake-cli' to push the Python Wheel file as a package, then add the package name to the list of packages when creating the stored procedure or UDF.

E. Upload the Python Wheel file to an external stage (e.g., AWS S3) and configure Snowflake to access the external stage, then reference the wheel file path in the USING' clause of the UDF or stored procedure.

正確答案: C,D

說明：（僅 VCESoft 成員可見）

問題7

You are using Snowpark Python to build a machine learning pipeline. One step in the pipeline involves feature engineering using a large dataset. This feature engineering step is computationally expensive and involves several transformations. You want to optimize the performance of this step by caching intermediate results. Given the following code snippet, which of the following strategies would be MOST effective for optimizing the performance, considering the use of

A. Identify DataFrames that are reused multiple times and cache them using after the transformations that generate them.

B. Cache the initial raw data DataFrame before applying any transformations.

C. Cache each intermediate DataFrame after each individual transformation step, even if the DataFrame is only used once.

D. Cache the final DataFrame only after all feature engineering steps are completed.

E. Avoid using altogether because it can introduce overhead and is not always beneficial.

正確答案: A

說明：（僅 VCESoft 成員可見）

問題8

You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

A. Option B

B. Option D

C. Option C

D. Option A

E. Option E

正確答案: A

說明：（僅 VCESoft 成員可見）

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