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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and Operations | 35% | - Advanced operations
|
| Topic 2: Snowpark Concepts and Architecture | 25% | - Session management and connection
|
| Topic 3: Performance and Best Practices | 10% | - Optimization techniques
|
| Topic 4: Snowpark API and Development | 30% | - Multi-language support
|
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NEW QUESTION # 337
You are tasked with building a data pipeline that uses Snowpark to process customer data in a table called 'CUSTOMERS'. The table contains sensitive information, and you need to ensure that any Personally Identifiable Information (PII) is removed from the table after 30 days. You decide to implement a mechanism to automatically delete records older than 30 days. Which of the following approaches would you consider when designing the deletion process with consideration of cost, performance and security?
Answer: A,C,D
Explanation:
Options B, C and E are viable and have tradeoffs. A and D will be less ideal. Option A : This approach can be very inefficient as it requires scanning the entire table daily to identify records for deletion. This can lead to high costs and performance issues. Option B : Using a Snowflake Task with a direct SQL DELETE statement is generally more efficient than reading the entire table in Snowpark, especially for large tables. Snowflake can optimize the DELETE operation internally. Option C : Encapsulating the deletion logic in a Snowpark Stored Procedure and scheduling it with a Snowflake Task allows you to leverage Snowpark's capabilities while benefiting from Snowflake's task scheduling and optimization. This can offer a good balance of flexibility and performance. Option D : This is an archive solution, not deletion; it's not as fast or space saving as truncating. Option E : Data masking is a secure alternative. It preserves the records while redacting sensitive data. No need to scan for old records, so its best on performance, cost, and security.
NEW QUESTION # 338
You have a Snowpark DataFrame 'customer df with a 'customer name' column. You need to create a new column 'initials' that contains the initials of each customer's name. For example, if 'customer name' is 'John Doe', 'initials' should be 'JD'. You must handle names with multiple words correctly. Which Snowpark SQL expression using the "col()' function is the most efficient and correct way to define the 'initials' column?
Answer: C
Explanation:
Option D is the most robust and efficient solution. It uses a regular expression to extract the first letter of each word in the name, handling multiple words correctly, and converting it to uppercase. Option A is incorrect because it only handles two-word names and relies on string concatenation, which can be less efficient than using the 'concat function. Option B is incorrect as 'array_accumulate' is not used in Snowpark to compute initials. Option C is incorrect as it is too basic and relies on two-word names only. Option E is incorrect as it extracts only the first two characters from the name.
NEW QUESTION # 339
You have a Python function named 'process data' that performs data cleaning and transformation on a Pandas DataFrame. You want to convert this function into a Snowpark Python stored procedure to leverage Snowflake's compute resources. However, the 'process_data' function relies on several external Python libraries (e.g., 'pandas', 'numpy', 'scikit-learn') that are not pre-installed in the Snowflake environment. Which of the following approaches would ensure that these dependencies are available within the Snowpark stored procedure? Choose all that apply
Answer: A,E
Explanation:
Options B and D are the correct ways to handle external dependencies for Snowpark Python stored procedures. Option B: The packages argument of the '@sproc' decorator or 'session.add_packages' method is the most straightforward way to specify dependencies. Snowflake will automatically download and install these packages from its Anaconda channel. Option D: Bundling libraries into a ZIP file and uploading it to a stage is a valid approach when you need to use specific versions of libraries or libraries that are not available in the Snowflake Anaconda channel. However, it requires careful management of library paths within the Python code. Option A is incorrect. Snowflake does not automatically resolve and install dependencies based solely on 'import' statements. Option C is a more complex approach and is generally not necessary unless you have very specific requirements for package versions or custom packages. It's often easier to use the 'packages' argument for standard libraries. Option E : there is no session.custom_package in snowpark python API.
NEW QUESTION # 340
You're developing a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and writes the result to another table. You notice that the application throws a 'net.snowflake.snowpark.exceptions.SnowparkClientException: JDBC driver encountered an unexpected error.' intermittently. Examining the Snowflake query history, you observe many queries failing due to 'Warehouse Suspended'. Which of the following strategies would BEST address this issue in a production environment?
Answer: B,C
Explanation:
Implementing retry logic handles transient connection errors gracefully. Explicitly resuming and suspending ensures the warehouse is available during the application's execution and properly suspended afterward, controlling costs and resources. Increasing AUTO_SUSPEND is a poor practice as it wastes credits. Scheduling during off-peak hours might reduce the frequency of suspensions but doesn't eliminate the problem. A larger warehouse might help, but cost is a factor and it doesn't directly address the intermittent suspension problem.
NEW QUESTION # 341
A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?
Answer: B
Explanation:
Snowflake Tasks are the recommended way to schedule stored procedures within Snowflake. They are a native Snowflake feature, providing scalability, reliability, and integration with Snowflake's monitoring and management tools. Airflow is a valid option, but adds external dependencies.
NEW QUESTION # 342
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