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Free PDF Quiz 2025 High Hit-Rate Snowflake DEA-C02 Reliable Braindumps Ppt

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q318-Q323):

NEW QUESTION # 318
You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

A. Add a search optimization strategy to the table on the 'event_time' column.
B. The table is already partitioned by 'event_time' , so there is no need for further optimization.
C. Increase the warehouse size to improve query performance.
D. Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
E. Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.

Answer: A,D,E

Explanation:
Partitioning in Snowflake is primarily for data management and micro-partition elimination on exact matches, not range queries. Clustering (B) reorders the data for better performance with range-based queries. A materialized view (C) pre-computes the aggregation, significantly speeding up the specific query. A search optimization strategy (E) can improve performance without requiring a full table scan. Increasing warehouse size (D) may help to some extent but is not the most targeted optimization. Option A is incorrect because partitioning alone doesn't solve the range query performance issue.

NEW QUESTION # 319
A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A. Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
B. Create separate tables for each "PRODUCT_ID' to improve query performance.
C. Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
D. Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
E. Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.

Answer: A

Explanation:
Analyzing the cardinality and data distribution (Option B) is crucial to understanding the effectiveness of search optimization. If 'PRODUCT_ID has skewed data distribution, search optimization might not be as effective. helps estimate the number of unique values, and histograms reveal the distribution. While estimating the cost of search optimization (Option A) is good practice, it doesn't directly address the potential skewness issue. Clustering (Option C) is a different optimization technique, and dropping/recreating the table (Option D) is a drastic measure without evidence of corruption. Creating separate tables for each 'PRODUCT_ID is not scalable and will drastically increase maintenance overhead.

NEW QUESTION # 320
You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

A. Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
B. Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
C. Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
D. Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
E. Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.

Answer: D

Explanation:
Option C is the most effective. 'pandas_udf with 'vectorized=True' allows processing data in batches using pandas DataFrames, significantly reducing the overhead of invoking the UDF for each row. Pre-initializing the library within the UDF's closure avoids repeated initialization. Increasing warehouse size (A) might help but is not as targeted. UDF caching (B) only helps if the inputs are identical and doesn't address the initialization overhead. Rewriting in SQL (D) might not be feasible if the third-party library is essential. Converting to Java (E) could help, but optimizing the Python code first is generally a better starting point.

NEW QUESTION # 321
You are tasked with creating a JavaScript stored procedure in Snowflake to perform a complex data masking operation on sensitive data within a table. The masking logic involves applying different masking rules based on the data type and the column name. Which approach would be the MOST secure and maintainable for storing and managing these masking rules? Assume performance is not your primary concern but code reuse and maintainability is the most important thing.

A. Hardcoding the masking rules directly within the JavaScript stored procedure.
B. Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.
C. Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
D. Defining the masking rules as JSON objects within the stored procedure code.
E. Using external stages and pulling the masking rules from a configuration file during stored procedure execution.

Answer: B,C

Explanation:
Options B and E are the most secure and maintainable. Storing the masking rules in a separate Snowflake table allows for easy modification and version control without altering the stored procedure code. Javascript UDFs make the logic reusable, maintainable and dynamic. Hardcoding the rules (A) makes maintenance difficult. JSON objects within code (C) are an improvement but are still embedded within the code. Using external stages (D) introduces dependencies and potential security risks if not managed carefully.

NEW QUESTION # 322
A data engineering team has implemented a continuous data pipeline that loads data into a Snowflake table named 'SALES DATA' They notice that the pipeline intermittently experiences performance degradation, particularly during peak business hours. The team wants to implement alerts to proactively identify and address these performance issues. Which of the following approaches would be MOST effective for monitoring the pipeline and triggering alerts based on specific performance metrics related to data loading?

A. Create a Snowflake Alert based on a metric in the Account Usage views (e.g., that identifies when load durations for the data warehouse associated with the data pipeline exceed a specified threshold. Configure a Notification Integration to route alerts to a designated channel.
B. Create a Snowflake Task that periodically queries the 'QUERY_HISTORY view, calculates the average load duration for 'SALES_DATX, and triggers an alert if the duration exceeds a predefined threshold. Use a Stored Procedure to handle the alert logic and send notifications.
C. Enable Snowflake's query acceleration service. This service automatically analyzes query performance and identifies opportunities for optimization, removing the need for manual monitoring and alerting. Use Snowflake's resource monitors to track credit usage.
D. Implement a data streaming service that monitors the 'SALES_DATX table in real-time. The streaming service should track the number of rows inserted per minute and trigger an alert if the insertion rate drops below a predefined threshold. No Snowflake object or Alert required.
E. create a custom Snowflake Alert that triggers when the function for the 'SALES_DATA' table indicates significant delay in data loading. Use a Snowflake Notification Integration to send alerts via email or Slack.

Answer: A,B

Explanation:
Options A and E offer the most effective approaches. Option A leverages Snowflake's Task and Stored Procedure capabilities to monitor query history and trigger alerts based on load duration. Option E utilizes Snowflake Alerts based on Account Usage views to monitor warehouse load history. Option B is not Snowflake native, and doesn't directly leverage snowflake alerting capabilities. Option C, while helpful for overall performance, doesn't directly address the specific alerting requirements of the scenario. Option D's 'SYSTEM$LAST CHANGE COMMIT TIME function may not provide granular enough information for performance monitoring.

NEW QUESTION # 323
......

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