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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

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

Answer: D

Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.

NEW QUESTION # 26
You are analyzing customer order data in Snowflake. The 'orders' table has columns: and 'order_totar. Your task is to identify the top 5 customers who have consistently placed high-value orders over time. You need to rank customers based on their average order total, but only consider customers who have placed at least 10 orders. Furthermore, you want to account for the recency of orders by applying a weighted average where more recent orders contribute more to the average. Which of the following approaches will efficiently achieve this goal in Snowflake?

A. Use a weighted average calculation involving a date-based weighting factor (e.g., days since the order date), calculate the average order total with this weighting, filter with COUNT( ) >= 10 using HAVING, and then rank using RANK() OVER (ORDER BY weighted_average_order_total DESC).
B. Calculate the average order total using AVG(), filter customers with COUNT( ) >= 10 using HAVING, then rank them using RANK() OVER (ORDER BY average_order_total DESC).
C. Use a QUALIFY clause in conjunction with window functions to filter customers with at least 10 orders and calculate both average and weighted average. Then use the ranking function over the weighted average.
D. Create a stored procedure to iterate through each customer, calculate the weighted average order total, and then rank them in the application layer.
E. Calculate the average order total and order count for each customer using a subquery, then join the results with a generated series of dates to calculate the weighted average in the outer query, finally ranking the customers using DENSE_RANK().

Answer: A,C

Explanation:
Both options B and E correctly address the problem. B calculates a weighted average, filters based on the minimum order count, and then ranks customers based on the weighted average. E achieves the same result in using QUALIFY which is an important technique to filter. Option A doesn't account for weighting. C is inefficient and does not leverage Snowflake's processing power. D is unnecessarily complex with join, date series and subquery for simple operation that can be achieved using window functions.

NEW QUESTION # 27
In diagnostic analysis, what significance do demographics and relationships hold when identifying anomalies? (Select all that apply)

A. Analyzing only recent demographic data for anomalies
B. Considering relationships among data variables
C. Ignoring data relationships for focused analysis
D. Identifying demographic variations linked to anomalies

Answer: B,D

Explanation:
Identifying demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.

NEW QUESTION # 28
The image shows a table with a variant column that is storing a JSON record:

This SQL query is run:

What will be the result?

Answer: C

Explanation:
To correctly predict the output of this query, a Data Analyst must understand how Snowflake handles semi- structured data (JSON), specifically regarding path notation, data type casting, and the display of VARIANT values in the Snowsight UI.
1. Variant vs. Casted Types (Display): When you access a value within a JSON object using colon notation (e.g., json_col:employee.phone[0]), the resulting value is of the VARIANT data type. In the Snowflake Snowsight results pane, VARIANT strings are displayed with double quotes (e.g., "+1 223-256-7330").
However, when you explicitly cast a variant to a string type using the double-colon syntax (e.g., ::varchar), Snowflake extracts the value as a native SQL string, which is displayed without quotes (e.g., +1 028-735-
4521).
2. Handling Empty Strings vs. Out-of-Bounds: In the provided JSON exhibit, the phone array contains four elements at indices 0, 1, 2, and 3. The elements at indices 2 and 3 are empty strings (""). When these are queried, Snowflake returns the empty string variant, which appears as "" in the result set. The query also attempts to access phone[4]. Since the array only has four elements (max index 3), index 4 is out-of-bounds.
In Snowflake, accessing a non-existent index in an array or a non-existent key in an object returns NULL.
Evaluating the Options based on the exhibit:
* Option B correctly reflects all these behaviors: WORK_PHONE is in quotes (Variant), OFFICE_PHONE1 is not in quotes (Casted), OFFICE_PHONE2 and OFFICE_PHONE3 show the empty strings present in the JSON, and OFFICE_PHONE4 correctly shows null for the out-of-bounds access.
* Option A is incorrect because it misses the quotes for the variant column and shows the out-of-bounds index as an empty string instead of null.
* Option C is incorrect because it fails to account for the empty strings present at indices 2 and 3.
* Option D incorrectly mixes the casting logic and display.
This question tests the "Schema-on-Read" proficiency required of a SnowPro Advanced: Data Analyst, specifically the ability to predict exactly how transformed semi-structured data will materialize for end-users.

NEW QUESTION # 29
How can a Data Analyst automatically create a table structure for loading a Parquet file?

A. Use the INFER_SCHEMA together with the CREATE TABLE LIKE command.
B. Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE LIKE command.
C. Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE USING TEMPLATE command.
D. Use INFER_SCHEMA together with the CREATE TABLE USING TEMPLATE command.

Answer: D

Explanation:
Manually defining table structures for complex semi-structured files like Parquet can be error-prone and time- consuming. Snowflake provides a specific automation workflow to handle this, involving the detection of the file's internal schema and the dynamic creation of a matching table.
The process starts with the INFER_SCHEMA function. Because Parquet files are self-describing, they contain metadata about their columns and data types. INFER_SCHEMA reads this metadata from files in a stage and returns a list of column names and types. To turn this list into an actual table, the analyst uses the CREATE TABLE ... USING TEMPLATE syntax. This command takes the output of INFER_SCHEMA as an input and automatically builds a table with the corresponding definition.
Evaluating the Options:
* Option A is incorrect because CREATE TABLE LIKE is used to copy the structure of an existing table
, not to build a new one from file metadata.
* Option C and D are incorrect because GENERATE_COLUMN_DESCRIPTION is a helper function used to create a formatted string of column definitions, but it is not the primary command used with USING TEMPLATE for automated table creation.
* Option B is the Correct answer. The combination of INFER_SCHEMA (to find the columns) and USING TEMPLATE (to build the table) is the standard Snowflake pattern for schema-on-read automation in Data Ingestion workflows.

NEW QUESTION # 30
......

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