Free Access Snowflake.DAA-C01.v2025-12-20.q172 Practice Test (Page 32)

Question 151

A data engineering team is implementing a complex data transformation pipeline using Snowflake tasks and streams. They need to monitor the execution of these tasks, track dependencies, and identify potential bottlenecks in near real-time. Which approach provides the MOST comprehensive solution for logging and monitoring the task execution and dependencies within Snowflake?

A.Relying solely on Snowflake's task history view (TASK_HISTORY) and manually analyzing the execution times and statuses of each task.

B.Creating custom logging tables within Snowflake to record task start and end times, along with status codes, and then querying these tables for monitoring purposes.

C.Utilizing Snowflake's Event Tables to capture TASK-related events, combined with an external monitoring tool (e.g., AWS CloudWatch, Azure Monitor) to visualize task dependencies and execution metrics.

D.Implementing a complex DAG management system outside of Snowflake and triggering Snowflake tasks from that system, using the external system's logging capabilities.

E.Simply enabling email notifications for task failures in Snowflake.

Question 152

You are tasked with building a dashboard that visualizes website traffic data stored in Snowflake. The data includes daily unique visitors, bounce rate, and average session duration. The business stakeholders want to understand the correlation between these metrics. They also want to identify any outliers or anomalies. Which chart type is BEST suited for identifying correlation and outliers in this dataset?

A.A line chart showing each metric over time.

B.A bar chart comparing the average values of each metric.

C.A scatter plot matrix showing the pairwise relationships between all metrics.

D.A pie chart showing the percentage contribution of each metric to the total.

E.A histogram showing the distribution of individual Metrics.

Question 153

In Snowflake, how does partition pruning contribute to optimizing query performance?

A.Impacts query planning but not execution

B.Limits data accessibility across warehouses

C.Filters unnecessary partitions during query execution

D.Increases query complexity and optimization

Question 154

You are designing a data warehouse in Snowflake for a retail company. The company has two tables: 'Transactions' (TransactionlD, CustomerlD, ProductlD, TransactionDate, Amount) and 'Products' (ProductlD, ProductName, CategorylD, Price). The 'Transactions' table contains millions of rows, and analysts frequently run queries that join these tables to analyze sales by product category. To optimize query performance and reduce data redundancy for these analytical queries, which of the following strategies would be MOST effective, considering Snowflake's architecture and best practices?

A.Create a materialized view that joins the 'Transactions' and 'Products' tables and includes relevant columns for analysis, partitioning the view by 'TransactionDates.

B.Create a standard view that joins the 'Transactions' and 'Products' tables, and rely on Snowflake's query optimizer to automatically optimize the join performance.

C.Create a clustered table that includes all columns in transactions and products tables using 'CLUSTER BY' clause to cluster most frequently used column.

D.Denormalize the 'Transactions' table by adding 'ProductName' and 'CategorylD columns directly to the 'Transactions' table using a data transformation pipeline after the initial load.

E.Create a stored procedure that automatically rebuilds the standard view daily using the latest data from the 'Transactions' and 'Products' tables.

Question 155

A financial institution uses Snowflake to store customer transaction data'. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the 'TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?

A.Create a materialized view that pre-aggregates the daily transaction volume and average transaction amount. Schedule a Snowflake task to refresh the materialized view hourly.

B.Create a regular view that calculates daily transaction volume and average transaction amount. Use a Snowflake stored procedure to execute the query and update a separate reporting table hourly.

C.Increase the warehouse size to X-Large and rely on Snowflake's query optimization engine without any changes to the data model or update process.

D.Create a temporary table that stores daily transaction summaries. Truncate and reload the temporary table hourly using a scheduled Snowflake task.

E.Implement caching within the dashboard application to store the query results and only refresh the data once a day to avoid performance issues

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