Instant Access Snowflake.DAA-C01.v2026-08-26.q67 Actual Practice Test Engine for Free (Page 3)

Question 6

There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?

A.Table_b has more rows than Table_a.
B.Table_b has a row-access policy defined.
C.A multi-cluster warehouse was used to run Query 1.
D.The USE_CACHED_RESULT was set to FALSE before running Query 2.

Correct Answer: B

In Snowflake, the performance of a metadata-based query (like MAX, MIN, or COUNT) is typically near- instantaneous because Snowflake maintains constant-time statistics in its Cloud Services layer. For a standard table, SELECT MAX(num) does not even require a virtual warehouse to be active; it simply reads the value from the table's metadata.
However, when a Row Access Policy (RAP) is applied to a table, the query's behavior changes fundamentally. A row access policy is a security feature that restricts which rows are visible to a user based on their role or other attributes. To enforce this policy, Snowflake can no longer rely on the high-level metadata of the entire table because it must first determine which specific rows the user is authorized to see.
Consequently, the query engine must scan the individual micro-partitions and evaluate the policy logic for every row (or block of rows) to filter out unauthorized data before calculating the maximum value. This turns a "metadata-only" operation into a data-scanning operation, which requires a running warehouse and significantly more time.
Evaluating the Options:
* Option A is incorrect because the prompt states the tables are "similarly-sized." Even if it were slightly larger, a metadata lookup for a standard table would still be nearly instant.
* Option C is incorrect because a multi-cluster warehouse helps with concurrency (multiple users), not the raw execution speed of a single simple aggregate query.
* Option D is incorrect because USE_CACHED_RESULT refers to the Query Result Cache. While turning it off would prevent a "0ms" response from a previous run, it wouldn't explain a "significant" slowdown compared to a standard metadata fetch.
* Option B is the 100% correct answer. The presence of a Row Access Policy forces a full data scan and policy evaluation, which is the most common reason for performance degradation in otherwise simple metadata queries.

Question 7

A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?

A.Inner join
B.Full outer join
C.Left outer join
D.Cross join

Question 8

You are designing a data pipeline that requires executing a sequence of SQL statements conditionally based on the success or failure of previous steps. You want to encapsulate this logic within a stored procedure in Snowflake. Which of the following approaches can be used to achieve this conditional execution within a stored procedure, and what are their key differences regarding error handling and control flow?

A.Using 'TRY...CATCH' blocks within the stored procedure to handle exceptions and control the flow based on error codes. Each SQL statement is wrapped in a TRY block, and if an error occurs, the CATCH block is executed to log the error and potentially execute alternative steps. This ensures that the procedure continues to execute even if some steps fail.
B.Using 'IF...THEN...ELSE' statements to check the 'SQLCODE' or 'SQLSTATE after each SQL statement. If the 'SQLCODE indicates an error, the procedure can execute alternative steps or terminate gracefully. This allows for fine-grained control over the execution flow based on the success or failure of individual statements.
C.Using Javascript as the stored procedure language and leveraging its built-in 'try...catcW blocks. SQL statements are executed using the Snowflake Javascript API, and Javascript's error handling mechanisms are used to manage exceptions and control the flow. This offers more flexibility in handling complex error scenarios and integrating with external systems.
D.Utilizing Snowflake's Task feature to define dependencies between different SQL operations. Each SQL statement is defined as a separate Task, and dependencies are created to ensure that Tasks are executed only if their preceding Tasks succeed. This provides a robust and scalable way to manage complex data pipelines with conditional execution.
E.All of the above approaches can be used, but they differ in terms of complexity, performance, and error handling capabilities.

Question 9

You are working with a table 'ORDERS' containing order data, and a table 'CUSTOMER SEGMENTS containing customer segment information. The 'ORDERS' table has columns 'ORDER ID', 'CUSTOMER ID, and 'ORDER AMOUNT'. The 'CUSTOMER SEGMENTS' table has columns 'CUSTOMER ID', 'SEGMENT ID', and 'SEGMENT NAME'. You need to create a query that enriches the 'ORDERS' table with the customer segment information. However, a customer can belong to multiple segments. You want to include all segments a customer belongs to in the enriched data, resulting in potentially multiple rows per order if the customer is in multiple segments. The output should include 'ORDER ID, 'ORDER AMOUNT, 'SEGMENT ID', and 'SEGMENT NAMES. Which SQL statement would correctly enrich the ORDERS table without losing any order information, even if customers belong to multiple segments?

A.
B.
C.
D.
E.

Question 10

You're tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called 'ETL LOGS' with columns 'end_time', and 'error_flag' (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?

A.Option A
B.Option B
C.Option C
D.Option D
E.Option E

Question 6

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Question 8

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Question 10

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