Reliable Snowflake DAA-C01 Exam Question - DAA-C01 Official Practice Test

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)
Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets
Build and Troubleshoot Advanced SQL Queries	20–25%	- Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing
Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Prepare and Load Data	15–20%	- Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation
Prepare and Present Data	10–15%	- Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements

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DAA-C01 Official Practice Test & DAA-C01 Prepaway Dumps

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

A. Use a window function to rank products by total revenue, then filter for the top 10 ranks using a 'QUALIFY' clause. Finally, calculate cumulative revenue using 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey.
B. Use the aggregation to identify top 10 'product_id' and calculate the cumilative revenue using SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)'.
C. Use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey to calculate the cumulative revenue for all products, then filter the results to include only the top 10 products based on their final cumulative revenue.
D. Create a temporary table to store the total revenue for each product, then select the top 10 from the temporary table. Join this result with 'sales_data' and apply ' SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey for the cumulative revenue calculation.
E. Use a subquery to find the top 10 'product_id' based on total revenue, then join this subquery with 'sales_data' and use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)' to calculate the cumulative revenue.

Answer: A

Explanation:
Option B is the most efficient. and 'QUALIFY allow filtering and ranking within a single query, and efficiently calculates the cumulative revenue. Option A requires a join which can be less performant. Option C involves creating a temporary table which adds overhead. Option D calculates cumulative revenue for all products before filtering, which is unnecessary work. Option E could be considered, however APPROX TOP_N is approximate and the requirement asks for an exact calculation.

NEW QUESTION # 47
You are working with clickstream data in Snowflake, stored in a table named This table has the following columns: (INTEGER), 'user_id' (INTEGER), (TIMESTAMP NTZ), 'event_type' (VARCHAR, e.g., 'page_view', 'button_click'), and 'session_id' (VARCHAR). A session is defined as a series of events by a single user with no more than 30 minutes between consecutive events. You need to determine the duration of each session in seconds. Which of the following SQL queries, when executed in Snowflake, will correctly calculate the duration of each session?

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

Answer: D,E

Explanation:
Options B and E are correct. Option B directly calculates the duration using 'DATEDIFF between the minimum and maximum timestamp within each session, grouped by Option E first calculates the previous event timestamp for each event within a session and calculates 'session_start' and 'session_end' correctly. Option A will result in errors since arithmetic operators are not allowed on TIMESTAMP_NTZ values. Option C does not aggregate the data by before subtracting start/end values. Option D is overly complex and also uses LAST VALUE which could result in errors since arithmetic operators are not allowed on TIMESTAMP NTZ values.

NEW QUESTION # 48
Which aspects are important when making predictions based on data for forecasting purposes?
(Select all that apply)

A. Incorporating statistical methods for accurate predictions
B. Considering trends and anomalies in historical data
C. Using only basic arithmetic functions for forecasting
D. Relying solely on historical data without considering external factors

Answer: A,B

Explanation:
Incorporating statistical methods and considering trends and anomalies are crucial for accurate predictions in forecasting.

NEW QUESTION # 49
You are tasked with creating a data model for a global e-commerce company in Snowflake. They have data on customers, products, orders, and website events. They need to support complex analytical queries such as 'What are the top 10 products purchased by customers in the US who have visited the website more than 5 times in the last month?' The data volumes are very large, and query performance is critical. Which of the following data modeling techniques and Snowflake features, used in combination, would be MOST effective?

A. A fully normalized relational model with primary and foreign key constraints, combined with Snowflake's automatic query optimization.
B. A star schema with fact and dimension tables, combined with materialized views to pre-aggregate data and clustering on dimension keys in the fact table.
C. A wide, denormalized table containing all customer, product, order, and event data, combined with Snowflake's zero-copy cloning for data backups.
D. A data vault model, combined with Snowflake's search optimization service on the hub tables.
E. A star schema with fact and dimension tables, combined with clustering the fact table on a composite key of customer ID and product ID.

Answer: B,E

Explanation:
Options B and E are the most effective. A star schema (B and E) is well-suited for analytical workloads. Clustering the fact table on customer and product IDs (B) helps improve query performance when filtering on those dimensions. Materialized views (E) provide pre-aggregated data for common queries, further boosting performance. Normalization (A) can lead to too many joins. Data Vault (C) is complex and may not be necessary. A wide, denormalized table (D) can be difficult to manage and maintain, and zero-copy cloning is for backup, not performance. Clustering on dimension keys in the fact table works best when coupled with a star schema and the keys are frequently used as a filter.

NEW QUESTION # 50
You are analyzing website traffic data stored in a Snowflake table named 'page views'. The table has columns 'user id', 'page_url', and 'timestamp'. You need to identify users who visited a specific sequence of pages ('/home', '[products', '/cart', '[checkout') within a 5- minute window. Which analytic function and additional Snowflake features would be MOST efficient and accurate to achieve this?

A. Use LAST VALUE and FIRST VALUE along with CASE statements, ordering by timestamp for each user, followed by joining the table to itself four times to find the page sequence.
B. Use 'SESSIONIZE to define user sessions based on a 5-minute inactivity gap. Then, use window functions to identify users who visited all four specified pages within the same session. This requires Snowflake Enterprise Edition.
C. Use to check previous page visits, ordered by 'timestamp' within each 'user_id' , and then filter based on the sequence. Create a user-defined function (UDF) in Python to handle the 5-minute window logic.
D. Create a stored procedure that iterates through each user's page views, sorted by timestamp, checking for the page sequence within the 5-minute window.
E. Use 'LEAD and 'LAG' functions to check the next and previous page visits, ordered by 'timestamp' within each , then use a 'QUALIFY' clause with conditional statements to identify the correct sequence and timeframe.

Answer: E

Explanation:
Option B is the most efficient and accurate. Using LEAD and LAG allows direct comparison of adjacent events in time, and the QUALIFY clause provides a concise way to filter based on complex conditions. Sessionize is also valid in Enterprise Edition. While UDFs (A) and stored procedures (D) are possible, they are generally less performant than SQL analytic functions. Option E, self-joining the table multiple times, would be inefficient and difficult to maintain.

NEW QUESTION # 51
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