Snowflake DEA-C02 問題集 : SnowPro Advanced: Data Engineer (DEA-C02)

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Snowflake DEA-C02 試験シラバストピック:

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

1. Given the following scenario: You have an external table 'EXT SALES in Snowflake pointing to a data lake in Azure Blob Storage. The storage account network rules are configured to only allow specific IP addresses and virtual network subnets, enhancing security. You are getting intermittent errors when querying 'EXT SALES. Which of the following could be the cause(s) and the corresponding solution(s)? Select all that apply.

A) The file format specified in the external table definition does not match the actual format of the files in Azure Blob Storage. Solution: Update the 'FILE_FORMAT parameter in the external table definition to match the correct file format.
B) The network connectivity between Snowflake and Azure Blob Storage is unstable. Solution: Implement retry logic in your queries to handle transient network errors.
C) The Snowflake service principal does not have the correct permissions on the Azure Blob Storage account. Solution: Ensure the Snowflake service principal has the 'Storage Blob Data Reader' role assigned to it.
D) The Snowflake IP addresses used to access the Azure Blob Storage are not whitelisted in the storage account's firewall settings. Solution: Obtain the Snowflake IP address ranges for your region and add them to the storage account's allowed IP addresses.
E) The table function cache is stale, causing access to non-existent files. Solution: Run 'ALTER EXTERNAL TABLE EXT_SALES REFRESH'.

2. A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)

A) Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.
B) Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
C) Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
D) Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
E) Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.

3. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Contact Snowflake Support and request restoration from Fail-safe.
B) Use the 'UNDROP DATABASE PRODUCTION command.
C) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
D) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
E) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.

4. You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.
B) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
C) Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
D) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
E) Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.

5. A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

A) Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
B) Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
C) Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
D) Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.
E) Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.

質問と回答：