DEA-C02 : SnowPro Advanced: Data Engineer

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題:

1. A data engineering team is running a series of complex analytical queries against a large Snowflake table. They notice that query performance is inconsistent, with some queries running much slower than others. After investigation, they determine that the queries are not properly leveraging the data clustering. Which of the following actions could improve the query performance related to the data clustering? Select all that apply.

A) Increase the virtual warehouse size to improve the performance of all queries, regardless of clustering.
B) Run 'ALTER TABLE DROP CLUSTERING KEY to remove the existing clustering key and allow Snowflake to automatically cluster the data.
C) Run 'SHOW TABLES LIKE ' ' and review the 'clustering_information' column to understand the current clustering depth and benefit.
D) Run 'ALTER TABLE CLUSTER BY (columnl, column2Y to explicitly define a clustering key on the table.
E) Execute SYSTEM$CLUSTERING INFORMATION( ' )' y to assess the impact of clustering on query performance and determine if re-clustering is needed.

2. You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A) Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
B) Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
C) Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
D) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
E) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.

3. You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

A) Avoid large or small number and stick to the limited range of input values.
B) Convert the input numbers to strings within the JavaScript UDF before performing the calculation.
C) Cast input arguments and the result to 'FLOAT within the UDF.
D) Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
E) Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.

4. Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

A) Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.
B) Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.
C) Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.
D) Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.
E) Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.

5. You're tasked with optimizing a Snowflake data pipeline that transforms and loads data into a target table. The pipeline uses a series of complex SQL queries with multiple joins and aggregations. After analyzing the query execution plans, you identify a few key bottlenecks. Which of the following optimization techniques would MOST directly address common performance bottlenecks in such a data pipeline within Snowflake?

A) Applying appropriate clustering keys to the target table, ensuring that commonly used filter columns are included in the clustering key definition.
B) Utilizing materialized views to pre-compute and store the results of expensive aggregations, and ensuring the query optimizer rewrites queries to use the materialized views where applicable.
C) Disabling the query result cache to ensure that the most up-to-date data is always used.
D) Increasing the virtual warehouse size to the largest available option (e.g., X-Large). The bigger the warehouse, the faster all queries will run.
E) Converting all SQL queries to use stored procedures for improved performance and security. Stored procedures execute faster than ad-hoc SQL.

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