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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and Operations | 35% | - User-defined logic
|
| Topic 2: Snowpark API and Development | 30% | - Python API fundamentals
|
| Topic 3: Performance and Best Practices | 10% | - Security and governance
|
| Topic 4: Snowpark Concepts and Architecture | 25% | - Session management and connection
|
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NEW QUESTION # 270
You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?
Answer: A,C
Explanation:
Using 'oauth_access_token' and 'private_key' (especially when stored securely) are more secure than directly passing username and password. OAuth and Key Pair authentication are recommended for production environments because they avoid storing or transmitting passwords directly. Options A & B are vulnerable because they expose credentials directly in the code or configuration. Option E is incorrect because simply setting the authenticator does not ensure the user authentication will happen with secure methods. User must use Oauth or Key pair authentication for Production use case.
NEW QUESTION # 271
You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?
Answer: D
Explanation:
Using a 'StructType' with 'StructField' for each metric is the most appropriate way to define the return type. This allows you to explicitly define the data type for each metric (e.g., 'FloatType' for 'average_price', 'Integer Type' for 'customer_count'), ensuring type safety and efficient data processing. 'VariantType' (Option A) would store the dictionary as a semi-structured data type, but you'd lose the benefits of explicit type definitions for each metric. 'MapType' (Option B) is more appropriate for representing a map with keys and values, not a fixed set of named metrics. Serializing to JSON (Option D) adds overhead and loses type information. 'ArrayType' (Option E) is not suitable for dictionaries. 'StructType' enforces a schema upon the returned data.
NEW QUESTION # 272
You have a Snowpark DataFrame containing customer order data with columns , and 'order_amount' . You need to identify customers who placed orders exceeding $1000 on more than 3 separate days. Which Snowpark code snippet correctly achieves this? Assume SparkSession 'spark' and DataFrame are already defined.
Answer: E
Explanation:
Option A correctly filters for orders exceeding $1000, groups by customer ID, counts the distinct order dates, aliases the count as 'distinct_order_days', filters for customers with more than 3 distinct order days meeting the criteria, and then displays the result. Option B counts total number of orders instead of distinct dates. Option C counts all orders exceeding $1000. Option D uses distinct which will remove some of the dates and produce innacurate number of days. Option E is an incorrect syntax.
NEW QUESTION # 273
You are developing a Snowpark application to process customer sentiment from text reviews. You have a Python function, , that utilizes a pre-trained NLP model loaded from a file on a Snowflake stage named This function returns a sentiment score (float) between -1 and 1. You need to register this function as a UDF so that it can be used within Snowpark DataFrames. Which of the following code snippets correctly registers the UDF, ensuring the NLP model is available to the function during execution?
Answer: D
Explanation:
Option E correctly uses the '@udf decorator with the 'imports' parameter to specify the location of the pickled model on the stage. It also uses to correctly construct the path to the imported file within the UDF's execution environment. Replace=True prevents errors if the UDF already exists. Options A, C and D don't correctly handle importing the NLP model. Option B has a security issue of loading file without validating the Path.
NEW QUESTION # 274
You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?
Answer: B,D
Explanation:
Increasing the warehouse size provides more compute resources for parallel processing. Analyzing the query plan using DataFrame.explain(Y helps identify bottlenecks (e.g., excessive data shuffling) so you can optimize the code. UDFs are not always more efficient than built-in functions and can sometimes introduce overhead. Reducing complexity and increasing the number of queries doesn't necessarily improve performance, especially if it leads to more I/O operations. Disabling result caching can negatively impact performance, as it prevents the reuse of previously computed results.
NEW QUESTION # 275
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