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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q261-Q266):

NEW QUESTION # 261
You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

A. Executing 'df.collect()' to load the entire table into the client's memory before filtering.
B. Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
C. Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE
D. Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.
E. Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.

Answer: E

Explanation:
Option C is the most effective. Data skipping works best when filters are applied early in the query execution plan. By filtering on the partition column CORDER DATE) before any joins or aggregations, Snowflake can effectively skip irrelevant partitions, significantly reducing the amount of data scanned. Applying the filter after joins (Option A) defeats the purpose of data skipping. Selecting columns (Option B) doesn't directly utilize data skipping. Caching (Option D) might help with subsequent operations but doesn't leverage data skipping itself. Collecting data (Option E) is highly inefficient for large tables and bypasses any server-side optimizations.

NEW QUESTION # 262
You are developing a data pipeline to ingest customer feedback data from a third-party service using the Snowflake REST API. This service imposes rate limits, and exceeding them results in temporary blocking. To handle this, you implement exponential backoff with jitter. Which of the following code snippets BEST demonstrates how to correctly implement exponential backoff with jitter when calling the Snowflake REST API in Python, assuming data)' is a function that makes the API call and raises an exception on rate limiting?

Answer: E

Explanation:
Option E correctly implements exponential backoff with jitter. It calculates the delay using 'base_delay 2 attempt (exponential backoff) and adds a random jitter using 'random.uniform(0, 1)'. It also handles non rate-limiting exceptions by re-raising the exception if it's not caused by rate limiting. Option A would fail to re-raise an error other than RateLimitException. Option B lacks jitter. Option C lacks both jitter and correct exponential backoff calculation. Option D does not use exponential backoff and also lacks retry logic. Therefore, option E is the correct answer.

NEW QUESTION # 263
You are designing a system to monitor data access patterns in Snowflake. You want to capture detailed information about all queries executed, including the user, query text, execution time, and any potential data access violations based on security policies. Which of the following approaches, used in combination, would provide the MOST comprehensive and scalable solution for this monitoring requirement? (Select TWO)

A. Implement Snowflake's Event Tables and configure them to capture security-related events, such as data access policy violations.
B. Configure the 'SNOWFLAKE database's audit logs and stream them to an external security information and event management (SIEM) system.
C. Enable query tagging and insert custom tags into each SQL statement indicating sensitive data access. Then, query 'QUERY HISTORY filtering on these tags.
D. Create a stored procedure to intercept all SQL commands before execution, log them, and then execute them using 'EXECUTE IMMEDIAT
E. Enable the 'QUERY_HISTORY view in the 'ACCOUNT_USAGE' schema and periodically query it using a scheduled task.

Answer: A,B

Explanation:
Snowflake's Event Tables are designed to capture specific events related to data access and security policy violations in a structured manner. These tables provide detailed insights into security-related activities. Configuring Snowflake's audit logs and streaming them to a SIEM system enables centralized security monitoring and analysis. This approach provides a comprehensive view of all security-related events across the Snowflake environment. Using 'QUERY _ HISTORY' will get only high level statistics. Query tagging adds overhead and is not comprehensive. Implementing a stored procedure to intercept SQL commands is not a scalable or recommended approach due to performance implications and potential security risks.

NEW QUESTION # 264
You have a 'SALES table and a 'PRODUCTS table. The 'SALES table contains daily sales transactions, including 'SALE DATE , 'PRODUCT ID', and 'QUANTITY. The 'PRODUCTS table contains 'PRODUCT and 'CATEGORY. You need to create a materialized view to track the total quantity sold per category daily, optimized for fast query performance. You anticipate frequent updates to the 'SALES table but infrequent changes to the 'PRODUCTS table. Which of the following strategies would provide the MOST efficient materialized view implementation, considering both data freshness and query performance?

A. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on 'SALE DATE.
B. Create two materialized views: one for daily sales by product and another joining the first with 'PRODUCTS' to aggregate by category. Cluster the first view by 'SALE DATE' and the second by 'CATEGORY'.
C. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE DATE and 'CATEGORY without any specific clustering key.
D. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on ' CATEGORY.
E. Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on 'SALE DATE' and 'CATEGORY.

Answer: A

Explanation:
Option B is most efficient. Clustering the materialized view on 'SALE_DATE will significantly improve query performance when filtering or grouping by date, which is a common operation in time-series data. Although frequent updates will affect the maintenance costs of the materialized view, querying on date will be very efficient. Option A is less efficient due to the lack of clustering. Option C may not be the best choice if filtering/grouping primarily occurs on date. Option D is also good, but Option B is better if most of the query filter is on SALE DATE. Option E introduces complexity and two refreshes may create a delay in data available.

NEW QUESTION # 265
You are tasked with implementing a data governance strategy in Snowflake for a large data warehouse. Your objective is to classify sensitive data columns, such as customer phone numbers and email addresses, using tags. You want to define a flexible tagging system that allows different levels of sensitivity (e.g., 'Confidential', 'Restricted') to be applied to various columns. Furthermore, you need to ensure that any data replicated to different regions maintains these classifications. Which of the following statements accurately describe best practices for implementing and maintaining data classification using tags in Snowflake, especially in a multi-region setup? Choose TWO.

A. Create a scheduled task that automatically identifies sensitive data based on regular expressions and applies the appropriate tags. This automates the classification process.
B. Tags and tag values must be uniquely defined across all schemas to avoid conflicts and ensure accurate data classification; Snowflake enforces uniqueness implicitly.
C. Define tag schemas at the account level and replicate them to all regions. This ensures consistency of tag definitions across the entire organization.
D. When replicating data between regions, the tags are automatically replicated along with the data, provided that replication is configured using database replication or failover groups including the tagging schema.
E. Always grant the ACCOUNTADMIN role to users who need to apply tags. This simplifies the process and ensures they have all necessary privileges.

Answer: C,D

Explanation:
Defining tag schemas at the account level (Option A) ensures consistency in tag definitions across the entire Snowflake account, including all regions. This is a best practice for managing tags in a multi-region environment. When replicating data between regions (Option C) using database replication or failover groups, the tags are automatically replicated along with the data, assuming the tagging schema is included in the replication configuration. Option B describes a valid approach to tag application automation, but it isn't a core best practice related to multi- region replication and tag management. Option D is incorrect because granting the ACCOUNTADMIN role provides excessive privileges and is not a recommended practice. Option E is incorrect because tag names need only be unique within their schema.

NEW QUESTION # 266
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