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Ted Tate Ted Tate

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DAA-C01 Prüfungsfragen, DAA-C01 Prüfungsübungen

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q53-Q58):

53. Frage
A data analyst is tasked with optimizing a query that aggregates data from a table 'ORDERS' containing order details, including columns like 'ORDER ID', 'CUSTOMER ID, 'ORDER DATE, 'PRODUCT ID', and 'QUANTITY. The query calculates the total quantity of products ordered per customer and month. The current query is as follows: SELECT CUSTOMER ID, DATE TRUNC('MONTH', ORDER DATE) AS ORDER MONTH, SUM(QUANTITY) AS TOTAL QUANTITY FROM ORDERS GROUP BY CUSTOMER_ID, ORDER_MONTH ORDER BY CljSTOMER_lD, ORDER_MONTH; Deopite the 'ORDERS' table being relatively small (10 million rows), the query performance is slow. The analyst suspects a poorly chosen warehouse size. Which of the following actions, combined with monitoring query execution, would be MOST beneficial to determine the optimal warehouse size and improve query performance?

A. Run the query multiple times with different warehouse sizes, recording the execution time for each size. Choose the warehouse size with the lowest execution time, regardless of cloud services usage.
B. Set the parameter to a low value to prevent long-running queries and force Snowflake to automatically optimize the warehouse size.
C. Increase the warehouse size to the largest available size and monitor query execution time and cloud services usage. If cloud services usage is high, decrease the warehouse size until a balance is achieved.
D. Start with the smallest warehouse size and incrementally increase the size, monitoring query execution time and cloud services usage. Stop increasing the size when query time plateaus or cloud services usage increases significantly.
E. Use the query history to determine the average execution time for similar queries and choose a warehouse size that is slightly larger than the one used for those queries.

Antwort: D

Begründung:
The most beneficial approach is to start with the smallest warehouse size and incrementally increase it (B). This allows for observing the impact of warehouse size on query performance and cloud services usage. Increasing until the query time plateaus or cloud services usage increases significantly indicates the point of diminishing returns. Simply using the largest size (A) may be wasteful, and ignoring cloud services usage (C) can lead to cost overruns. Query history (D) may not be relevant if the query is significantly different. Setting a timeout (E) will not optimize the warehouse size.

54. Frage
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?

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

Antwort: B

Begründung:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.

55. Frage
You are designing a dimensional model for a subscription-based service. You have a 'FACT SUBSCRIPTIONS' table with columns like 'subscription_id', 'customer id', 'start date', 'end date', and 'subscription_amount'. The business wants to analyze monthly recurring revenue (MRR) and churn rate. You need to model the temporal aspect of subscriptions to accurately calculate these metrics. Select the TWO best approaches to model the time dimension to facilitate these calculations:

A. Create a 'DIM_SUBSCRIPTIOW table with 'subscription_id' as the primary key and store all subscription details there, avoiding the need for a fact table.
B. Store 'start_date' and 'end_date' as VARCHAR columns in the 'FACT SUBSCRIPTIONS table to avoid data type conversions.
C. Create a 'DIM MONTH' table with columns like 'month id', 'month start date', and 'month end date' and link the 'FACT SUBSCRIPTIONS table to it based on the 'start_date' falling within the month.
D. Create a 'DIM_DATE table and link 'FACT_SUBSCRIPTIONS' to it using 'start_date' and 'end_date' columns.
E. Create a snapshot fact table FACT SUBSCRIPTION SNAPSHOTS that captures the state of each subscription at the end of each month. This table would include 'subscription_id' , 'customer_id', 'snapshot_date', 'is_active' , and 'subscription_amount' .

Antwort: C,E

Begründung:
Creating a snapshot fact table (option B) allows for direct calculation of MRR and churn at a specific point in time. Analyzing subscription state at monthly intervals is helpful for these metrics. Creating a 'DIM_MONTH' table (option E) simplifies grouping and aggregation of subscriptions by month. Option A (linking to 'DIM DATE' using both start and end dates) might be useful for other types of analysis, but not directly for MRR/churn calculations. Storing dates as VARCHAR (option C) is bad practice and will hinder performance. A dimension table for subscriptions (option D) won't capture the temporal changes necessary for these metrics.

56. Frage
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. Cross join
B. Inner join
C. Full outer join
D. Left outer join

Antwort: C

Begründung:
In Data Transformation and Data Modeling, selecting the correct join type determines how the query handles unmatched records from the participating tables. The requirement here is to return all records from both the projects table and the employee table.
A FULL OUTER JOIN (often shortened to FULL JOIN) is designed specifically for this purpose. It combines the results of both a LEFT OUTER JOIN and a RIGHT OUTER JOIN. It returns:
* Rows where there is a match between the project and the employee.
* Rows from the projects table that have no matching employees (padded with NULL in the employee columns).
* Rows from the employees table that are not assigned to any projects (padded with NULL in the project columns).
Evaluating the Options:
* Option A (INNER JOIN) only returns rows where there is a match in both tables. Any projects without employees or employees without projects would be excluded.
* Option C (LEFT OUTER JOIN) would return all projects, but would exclude employees who are not assigned to a project.
* Option D (CROSS JOIN) creates a Cartesian product, matching every single employee with every single project regardless of actual relationships. This would create a massive, redundant dataset and is not what is requested.
* Option B is the 100% correct answer. It ensures total data visibility from both entities, which is often required in data quality audits or comprehensive resource allocation reporting where the analyst needs to see "unlinked" data on both sides of the relationship.

57. Frage
A company is looking for new headquarters and wants to minimize the distances employees have to commute.
The company has geographic data on employees' residences. Through the Snowflake Marketplace, the company obtained geographic data for possible locations of the new headquarters. How can the distance between an employee's residence and potential headquarters locations be calculated in meters with the LEAST operational overhead?

A. ST_DISTANCE
B. ST_HAUSDORFFDISTANCE
C. ST_LENGTH
D. HAVERSINE

Antwort: A

Begründung:
Snowflake provides native support for geospatial analysis through the GEOGRAPHY and GEOMETRY data types, along with a suite of Standardized Spatial Functions. To calculate the "least distance" between two geographic points (such as an employee's home and a potential office site) on the Earth's surface, the most efficient and direct function is ST_DISTANCE.
The ST_DISTANCE function takes two GEOGRAPHY objects as input and returns the minimum geodesic distance between them. A key benefit of using this native function for Data Analysis is that it automatically returns the result in meters by default when used with the GEOGRAPHY type, which models the Earth as a spheroid. This eliminates the need for manual mathematical conversions or complex custom logic, satisfying the "least operational overhead" requirement.
Evaluating the Options:
* Option A (ST_HAUSDORFFDISTANCE) is used to measure the similarity between two shapes (geometries), not the simple distance between two points.
* Option B (HAVERSINE) is a mathematical formula that can be implemented manually in SQL, but it requires significantly more code and "operational overhead" compared to a single built-in function.
* Option C (ST_LENGTH) is used to measure the total length of a LineString or the perimeter of a Polygon, rather than the distance between two distinct objects.
* Option D is the 100% correct answer. It is the optimized, native Snowflake function for point-to-point distance calculations in the Data Cloud.
?

58. Frage
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