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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Tableau integration 2. Power BI integration 3. Other partner visualization tools - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Topic 2: Data Ingestion and Data Preparation	15%-20%	- Implement data processing solutions 1. Respond to processing failures 2. Cleanse, conform, and enrich data 3. Automate and implement data pipelines (scheduling) 4. Use logging and monitoring solutions (auditing, data lineage) - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Create tables and views 3. Find external data sets that correlate with available data - Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from structured sources (CSV) 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Synthetic Data Generation - Use best practice considerations relating to data integrity structures 1. Define primary keys for tables 2. Implement constraints 3. Perform table joins between parent/child tables - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Perform data discovery to identify what is needed from available datasets 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 2. Identify elements required for business goals using BI reports or SQL analysis 3. Determine the level of data granularity required 4. Query tables to assess data elements and statistics maintained by Snowflake 5. Evaluate required transformations (table joins, set operations, ASOF JOINS)
Topic 3: Data Transformation and Data Modeling	22%-30%	- Design data models 1. Snowflake schema design 2. Star schema design 3. Data vault models - Transform data using SQL 1. PIVOT/UNPIVOT operations 2. QUALIFY clauses 3. Common Table Expressions (CTEs) 4. Window functions
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
A data analyst is tasked with identifying the top 3 performing sales representatives in each region based on their total sales amount. The sales data is stored in a table named 'sales data" with columns 'region', 'sales_rep', and 'sales_amount'. Which Snowflake SQL statement(s) would efficiently achieve this?

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

Answer: A,B,D

Explanation:
Options A, B, and C correctly utilize window functions to rank sales representatives within each region. assigns a unique rank, assigns the same rank to ties and skips the subsequent ranks, and 'DENSE RANK()' assigns the same rank to ties but does not skip ranks. All three can be used with a 'WHERE clause to filter for the top 3. Option D incorrectly uses 'LAG' and doesn't achieve the desired ranking. Option E uses 'NTILE' which divides the rows into three groups; filtering 'tile = 1' might not always return the top three based on sales.

NEW QUESTION # 52
You have developed a Snowsight dashboard for your marketing team that contains sensitive customer data'. You need to share this dashboard with a specific group of users, but ensure that they can only view the data and cannot modify the dashboard itself or the underlying queries. Which of the following steps should you take to securely share the dashboard?

A. Share the dashboard with 'Can view' permission and no further action is required.
B. Share the dashboard with the group using the 'Can view' permission. Then, grant the users in the group the 'MONITOR privilege on the virtual warehouse used by Snowsight.
C. Share the dashboard with the group using the 'Can edit' permission. Then, grant the users in the group the 'USAGE' privilege on the database and schema containing the data.
D. Share the dashboard with the group using the 'Can view' permission. Ensure the group has the 'SELECT' privilege on the tables/views used in the queries and the ' USAGE privilege on the database and schema. Also ensure that any intermediate tables created by the dashboard are also granted these privleges.
E. Create a scheduled task to export the dashboard as a PDF and email it to the group on a daily basis.

Answer: D

Explanation:
Sharing with 'Can view' (B) ensures the users cannot modify the dashboard itself. Granting 'SELECT on the underlying tables/views and USAGE' on the database/schema allows them to see the data when the dashboard executes queries on their behalf. 'Can edit' (A) would allow them to modify the dashboard, which is not desired. 'MONITOR (C) is for monitoring warehouse usage, not data access. Exporting as PDF (D) is a workaround, but it loses interactivity and isn't a direct sharing method. E isn't correct because permissions on the data sources must be granted separately.

NEW QUESTION # 53
You're working with a large dataset containing website user activity, including 'session_id', 'user_id', 'timestamp', and 'page_view'. You suspect bot activity is skewing your engagement metrics. Bots tend to have very short session durations and high page view counts within those sessions. Which of the following Snowflake SQL queries, used in combination, would be MOST effective in identifying and flagging potential bot sessions, considering performance on a large dataset? Assume you have access to Snowflake's statistical functions.

Answer: E

Explanation:
Option E is most effective because it identifies bot sessions based on two distinct criteria: unusually high page view counts AND unusually short session durations, which are characteristic of bots. By combining these two conditions with standard deviations, it reduces the likelihood of false positives. All other answer choices only check rate and not the count or session duration .

NEW QUESTION # 54
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 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.
B. Create a 'DIM_DATE table and link 'FACT_SUBSCRIPTIONS' to it using 'start_date' and 'end_date' columns.
C. 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.
D. Store 'start_date' and 'end_date' as VARCHAR columns in the 'FACT SUBSCRIPTIONS table to avoid data type conversions.
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' .

Answer: A,E

Explanation:
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.

NEW QUESTION # 55
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. 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.
B. 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.
C. 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)'.
D. 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.
E. 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.

Answer: D

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 # 56
......

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