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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q321-Q326):

NEW QUESTION # 321
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. Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.
B. 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.
C. Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.
D. Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
E. Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.

Answer: B,E

Explanation:
Enabling Automatic Clustering on 'TRANSACTIONS with the specified key order ('TRANSACTION DATES, 'MERCHANT_CATEGORY , 'CUSTOMER_ID') aligns the data layout with common query patterns, allowing Snowflake to efficiently prune irrelevant data during query execution. This drastically improves query performance. Enabling Search Optimization on the 'MERCHANT_CATEGORY further enhances query performance by creating search access paths that enable faster lookups and filtering based on merchant category. Simply increasing the warehouse size (option A) may provide some improvement, but it's less targeted and potentially less cost-effective than optimizing the data organization. While dedicated warehouses (option C) can improve concurrency, they do not address the underlying performance bottleneck related to data access. Materialized views (option E) can be beneficial, but they require careful design and maintenance, and they might not be flexible enough for ad-hoc queries with varying filter conditions. Clustering and search optimization provide a more general and efficient solution in this scenario.

NEW QUESTION # 322
You have a 'WEB EVENTS' table that stores user activity on a website. It includes columns like 'USER ID, 'EVENT TYPE , EVENT TIMESTAMP, and 'PAGE URL'. You need to create a materialized view that calculates the number of distinct users visiting each page daily. You are also tasked with minimizing the impact on the underlying 'WEB EVENTS' table during materialized view refreshes, as other critical processes rely on it. Which of the following strategies would provide the MOST efficient solution, considering both performance and concurrency?

A. Create a standard materialized view that calculates the distinct user count per page daily directly from the 'WEB EVENTS table without any special configuration.
B. Create a materialized view and configure it to incrementally refresh, leveraging Snowflake's automatic refresh capabilities without any explicit scheduling.
C. Create a materialized view with a 'REFRESH COMPLETE strategy to ensure full data consistency after each refresh, even though it may lock the underlying table.
D. Create a task that truncates and reloads the materialized view daily. This ensures data consistency and prevents incremental refresh issues.
E. Create a materialized view and schedule regular, small batch refreshes to minimize lock contention and resource consumption on the 'WEB_EVENTS' table.

Answer: B

Explanation:
Option D provides the most efficient solution. Incremental refreshes are designed to efficiently update the materialized view with only the changes from the base table, minimizing the impact on the 'WEB_EVENTS' table. Options A, B and E might lock the table for longer periods. Option C is not a valid option in Snowflake. Option E is not efficient since it involves truncating and reloading the materialized view, consuming unnecessary resources and being potentially slow.

NEW QUESTION # 323
You are designing a Snowflake alert system for a data pipeline that loads data into a table named 'ORDERS'. You want to trigger an alert if the number of rows loaded per hour falls below a threshold, indicating a potential issue with the data source. You need to create an alert that is triggered based on the count of rows. Consider the code snippet below and the additional requirements. Assume that the table exists and the connection is successful.

A. You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.
B. Use Snowflake's Resource Monitor feature and adjust the credit quota to trigger an alert if the credit usage exceeds the threshold for the virtual warehouse processing data pipeline, indirectly indicating that performance is degraded or data volume has changed significantly.
C. Create a Snowflake Alert that executes a SQL query to count the number of rows loaded into the 'ORDERS table within the last hour. Configure the alert to trigger when the count is below the defined threshold. Use a Notification Integration to send alerts to a monitoring system.
D. Create a Snowflake Stream on the 'ORDERS' table. Then create an Alert that triggers based on the metadata column, comparing it to the threshold value. This allows for real-time monitoring of data changes.
E. Create a Snowflake task that runs every hour, executes a query to count the rows loaded in the past hour and triggers an alert using 'SYSTEM$SEND_EMAIC if the count is below the threshold. No need to create a Snowflake alert.

Answer: C

Explanation:
Option D directly addresses the requirements by creating a Snowflake Alert that monitors the row count within the last hour and triggers when the threshold is breached. The alert can then be integrated with a notification system for timely alerts. Option A is incorrect as Snowflake supports alert conditions using rolling windows via SQL functions (e.g., "last_altereff). Option B describes a task-based solution, not using the Snowflake alert object directly. Option C is incorrect because streams track changes, not the total number of rows loaded within a period. Option E is an indirect method and does not precisely measure rows inserted; therefore, it does not satisfy the requirement of alerting based on the number of rows loaded.

NEW QUESTION # 324
You are tasked with building an ETL pipeline that ingests JSON logs from an external system via the Snowflake REST API. The external system authenticates using OAuth 2.0 client credentials flow. The logs are voluminous, and you want to optimize for cost and performance. Which of the following approaches are MOST suitable for securely and efficiently ingesting the data?

A. Use Snowflake's Snowpipe with REST API by configuring the external system to directly push the logs to an external stage and configure Snowpipe to automatically ingest it.
B. Implement a custom API gateway using a serverless function (e.g., AWS Lambda, Azure Function) to handle authentication and batch the JSON logs before sending them to the Snowflake REST API. Write the API output to a Snowflake stage, then use COPY INTO to load into a final table.
C. Create a Snowflake external function that handles the API call and OAuth authentication. Use a stream on the external stage pointing to the external system's storage to trigger data loading into the final table.
D. Use the Snowflake REST API directly from your ETL tool, handling OAuth token management in the ETL tool. Load data into a staging table, then use COPY INTO with a transformation to the final table.
E. Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.

Answer: B,D

Explanation:
Options A and C are the most suitable. Option A involves direct integration and option C introduces batching and serverless function to improve performance and manage authentication. Option B is incorrect because external functions cannot directly trigger data loading based on external stage events. Option D bypasses the REST API requirement and does not address authentication. Option E avoids the REST API entirely, which is against the problem requirement.

NEW QUESTION # 325
Your company has a Snowflake account in the AWS cloud (us-west-2). You are planning to implement a disaster recovery strategy by replicating data to a separate Snowflake account in the Azure cloud (eastus2). You need to replicate multiple databases and shared objects. Which of the following steps are REQUIRED to configure and manage the replication process successfully? (Choose all that apply)

A. Configure network policies in both AWS and Azure accounts to allow communication between the Snowflake instances, particularly ingress and egress rules.
B. Create a storage integration in the target Azure account and grant the 'USAGE privilege on it to the replication group.
C. Create a secondary database in the target Azure account using the 'CREATE DATABASE AS REPLICA OF command.
D. Create a replication group in the source AWS account and add the databases and shared objects to it.
E. Grant the REPLICATE privilege on the source AWS account to the account locator of the target Azure account.

Answer: A,D,E

Explanation:
A, C, and D are required steps. Option A: Creating a replication group is essential to define what to replicate. Option C: The REPLICATE privilege allows the target account to pull data from the source. Option D: Network policies are crucial for establishing secure communication. Option B is incorrect; you create a secondary database using 'CREATE DATABASE AS REPLICA OF : after enabling replication on the source, not before. Option E is related to data loading from external stages, not replication itself in this direct account-to-account scenario.

NEW QUESTION # 326
......

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