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[General] New Guide SPS-C01 Files & New SPS-C01 Test Pdf

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【General】 New Guide SPS-C01 Files & New SPS-C01 Test Pdf

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q261-Q266):NEW QUESTION # 261
You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.
  • A. Extract the VARIANT data into a Pandas DataFrame and then use Pandas to access the nested fields before creating the Snowpark DataFrame.
  • B. Use the 'fieldl .field2')' function within the SELECT statement in the SQL query.
  • C. Use the "LATERAL FLATTEN(input payload)' function within the SQL query to unnest the VARIANT and then access the fields.
  • D. Use the 'payload:fieldl :field2 syntax directly within the SELECT statement in the SQL query.
  • E. First create a temporary table containing only the extracted fields using a separate SQL query, then create a Snowpark DataFrame from that table.
Answer: B,C,D
Explanation:
Options A, B, and D are correct. Option A utilizes the Snowflake's native dot notation (e.g., 'payload:fieldl :field2) for direct access of nested fields. Option B provides the 'GET_PATH' function, also allowing access to nested fields. Option D leverages 'LATERAL FLATTEN' to unnest the VARIANT data, enabling subsequent field access. Option C is less efficient, adding unnecessary steps, and Option E involves Pandas, which is typically not the optimal path for leveraging Snowpark's capabilities directly. Remember that 'LATERAL FLATTEN' is best when you need to process the data in a relational format after extracting it from the VARIANT.

NEW QUESTION # 262
A data engineer is tasked with creating a Snowpark session using JWT authentication. They have a private key 'rsa_key.pff, a user name 'snowpark_user' , and an account identifier 'my_account'. The goal is to create a session object suitable for submitting Snowpark jobs. Which code snippet correctly demonstrates the instantiation of a session object using JWT?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: C
Explanation:
Option B correctly reads the private key file, converts it to the required PEM format, and then uses it within the connection parameters to establish a Snowpark session. It handles the private key securely by loading and formatting it properly before passing it to the connection parameters. Option A, C and D attempts to directly provide the path to the private key or read the content with incorrect formatting which is incorrect. Option E doesn't address reading the private key in correct form and only address warehouse selection after the session is create.

NEW QUESTION # 263
You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: B
Explanation:
Option A correctly uses the 'flatten' function to unnest the 'viewed_products' array. It then accesses the 'product_id' within each flattened element using Tvalue']['product_idT. Options B, C, D and E either uses wrong function explode or does not handle nested structure properly or converts to varchar prematurely.

NEW QUESTION # 264
You are developing a Snowpark application to analyze customer data'. You need to create a Snowpark DataFrame from a list of dictionaries, where each dictionary represents a customer with 'id', 'name', and 'city' keys. The data should be loaded efficiently. Consider these scenarios: 1 . The input data can sometimes contain missing values (e.g., a customer might not have a city specified). 2. You want to ensure optimal performance when loading the data, as the list can be very large. 3. You need the resulting DataFrame's schema to correctly infer the datatypes based on the input dictionary's values. Which of the following methods and considerations should be used to create a Snowpark DataFrame from a list of dictionaries to meet these requirements?
  • A. Use 'session.createDataFrame(data)' and then explicitly cast columns with potential missing values to the correct datatype using method to ensure 'NULL' handling.
  • B. Use 'session.createDataFrame(data)' with default settings. Snowflake will automatically infer the schema and handle missing values as 'NULL'.
  • C. Use 'session.createDataFrame(data, where 'schema' is explicitly defined to handle missing data and ensure correct data types. This improves performance over schema inference.
  • D. Leverage Snowpark's optimized data loading by converting the list of dictionaries to a Pandas DataFrame first and then create a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Pandas has optimized data loading.
  • E. Use 'session.createDataFrame(data, and specify the 'nullable' property for each field within the schema. Additionally, define the data type explicitly. This optimizes performance.
Answer: C,E
Explanation:
Options B and D are the most appropriate. Providing an explicit schema, including specifying the 'nullable' property and datatypes, offers several advantages: 1. Handles missing values: By explicitly setting 'nullable=True' in the schema for columns that might contain missing values, you ensure that Snowflake correctly handles these as 'NULC. 2. Optimizes Performance: Specifying the schema avoids Snowflake's need to infer it, which can be a performance bottleneck, especially for large datasets. Explicit datatypes also help with storage and processing efficiency. 3. Data Type Control: You can ensure the correct data types are used for each column, preventing potential issues with data type conversions later om Option A relies on automatic schema inference, which can be inefficient and may not always correctly handle missing values or data types as expected. Option C requires explicit casting after DataFrame creation which is less efficient than specifying in the schema initially. Option E can add an unneccessary overhead since converting to Pandas DataFrame and then to Snowpark DataFrame may not be optimized.

NEW QUESTION # 265
You have a Snowpark DataFrame 'employees df representing employee data'. You need to update the 'salary' column for employees in the 'Sales' department by applying a 10% increase. Which of the following Snowpark code snippets correctly performs this update? Assume a Snowflake table named 'employees' exists and 'employees df' is correctly created from it.

  • A. Option C
  • B. Option A
  • C. Option B
  • D. Option D
  • E. Option E
Answer: A,E
Explanation:
Options C and E are correct. Snowpark does not have an 'update' method directly on DataFrames like some other Spark-based DataFrame implementations. The correct way to update the data is to use 'with_column' along with 'when' and 'otherwise' from 'snowflake.snowpark.functions' to conditionally update the 'salary' column based on the 'department'. The part is crucial to persist the changes back to the Snowflake table after the transformation. Option C imports the required functions using 'from snowflake.snowpark.functions import when, cor and Option E directly uses the dataframe to invoke function and column names. Option A and D are incorrect because Snowpark does not provide an 'update' or direct save after filtering like that for this type of update. Option B is also incorrect because the update function doesn't exist on dataframes.

NEW QUESTION # 266
......
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