Snowflake SPS-C01

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三つのバージョン

我々会社のSnowflake Certified SnowPro Specialty - Snowpark試験勉強資料はお客様に3種類のバージョンを提供します。第一種はPDF版で、お客様は印刷してから、紙質の形式で勉強し、メモをできます。第二種はSnowflake Certified SnowPro Specialty - Snowpark ソフト版で、第一時間に真実の試験解答環境と流れを感じさせることができます。第三種はオンライン版で、お客様はスマートとIPADなどの電子設備の上に使用されます。我々社のSnowflake Certified SnowPro Specialty - Snowparkオンライン版はオフライン使用をサポートします。

Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題:

1. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
B) Create an external function and grant access to the API integration that provides the security context.
C) Create a UDF and grant USAGE on the stage to the role that owns the UDF.
D) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
E) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.

2. 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 authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.
C) Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.
D) OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.
E) Using username/password authentication is the most secure and recommended approach for production environments.

3. You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

A)

B)

C)

D)

E)

4. You are working with a Snowpark DataFrame containing product review data'. The DataFrame has a 'review_text' column containing unstructured text reviews. Your task is to perform sentiment analysis on these reviews using Snowpark for Python. However, you are restricted to using only Snowpark built-in functions and UDFs; you cannot use external libraries like NLTK or TextBlob directly within your Snowpark code. Given this contraint, what is a valid approach to enrich your dataframe?

A) Create a Java UDF within Snowflake that utilizes a Java-based sentiment analysis library (e.g., Stanford CoreNLP) to process the 'review_text' and return the sentiment score. Then, call this Java UDF from your Snowpark Python code.
B) Build a Snowpark Python UDF that uses regular expressions to identify keywords and phrases indicative of positive, negative, or neutral sentiment. Assign a score based on the presence and frequency of these keywords. You can then apply 'when' statements to get .
C) Leverage Snowflake's external function capabilities to call a pre-trained sentiment analysis model hosted on a cloud ML platform (e.g., AWS SageMaker, Azure Machine Learning), passing the 'review_text' as input and receiving the sentiment score as output.
D) Develop a sentiment analysis microservice deployed outside Snowflake, then use Snowpark's 'call_udf function to invoke this service, passing the 'review_text' as input and receiving the sentiment score as output.
E) Use Snowpark's 'transform' function with a custom Python transformer that leverages an internal vocabulary of positive and negative words to assign a sentiment score to each review based on word frequency. Then store the result in the column.

5. You have a Snowpark DataFrame containing sensor data'. You need to write this data to a Snowflake stage 'sensor_stage' , creating a new set of files every hour based on the 'timestamp' column (data type: Timestamp). You also want to ensure that the file names include the hour of the timestamp and are written in Avro format with Zstandard compression. The directory structure on the stage should reflect the hourly partitioning. Which of the following approaches offers the most efficient and scalable way to achieve this, while minimizing the number of files written per hour?

A) Using scala user defined function (UDF) for write dataframe into stage in avro file format partitioned by Hour and calling it in snowpark dataframe.
B) Write a Python script that connects to Snowflake, retrieves the entire DataFrame, iterates through each row, determines the hour from the 'timestamp', and writes each row to a separate Avro file named after the hour in the 'sensor_stage'
C) Create a view on top of the data and schedule a task which create file in avro with zstd compression by running the select statment with group by hour.
D) Define a stored procedure that iterates through hourly intervals, filters the DataFrame based on the current hour, and writes the filtered DataFrame to the stage using 'df.write.format('avro').option('compression', 'zstd').mode('append').save(f'@sensor_stage/hour={current_hour}/')'
E) Create a new DataFrame by adding an 'hour' column extracted from the 'timestamp' column. Then use 'df.write.partitionBy('hour').format('avro').option('compression', 'zstd').mode('append').save('@sensor_stage/')'.

質問と回答：