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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. Aggregate functions 2. Time-series analysis 3. Snowflake-specific analytical features
Topic 2: Data Ingestion and Data Preparation	15%-20%	- 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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 3. Evaluate required transformations (table joins, set operations, ASOF JOINS) 4. Query tables to assess data elements and statistics maintained by Snowflake 5. Determine the level of data granularity required - Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Define primary keys for tables 3. Implement constraints - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from structured sources (CSV) 3. Synthetic Data Generation 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Implement data processing solutions 1. Respond to processing failures 2. Cleanse, conform, and enrich data 3. Use logging and monitoring solutions (auditing, data lineage) 4. Automate and implement data pipelines (scheduling) - 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
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%	- Transform data using SQL 1. Window functions 2. Common Table Expressions (CTEs) 3. PIVOT/UNPIVOT operations 4. QUALIFY clauses - Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?

A. SELECT user_name, SUM(credits_used_cloud_services) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time >=DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
B. SELECT user_name, SUM(credits_used_compute) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
C. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
D. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.warehouse_metering_history WHERE start_time >= DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
E. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.execution_history WHERE start_time >= DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;

Answer: B

Explanation:
The correct query should use the 'snowflake.account_usage.query_history' view and specifically sum the column as this reflects the credits used for the compute resources by query. Option C accurately reflects this. Option A is incorrect because the 'credits_used' column in 'query_history' does not give a direct credit consumption cost based on computation. Option B uses 'execution_history' which does not aggregate the same detailed credit usage information. Option D sums credits used for cloud services not the compute. Option E references warehouse metering history, not specific users' query execution history.

NEW QUESTION # 12
Your company needs to comply with GDPR regulations, and you're tasked with implementing data masking to protect sensitive customer information. Specifically, you need to mask email addresses in a 'CUSTOMERS' table during data preparation. Which of the following approaches, utilizing Snowflake's system functions and data masking capabilities, offers the MOST secure and flexible solution, while allowing authorized users to potentially unmask the data if necessary?

A. Use the 'MD5()' function to encrypt the email addresses. Authorized users can decrypt the MD5 hash using the appropriate key.
B. Create a view that uses 'REGEXP REPLACE()' function to redact portions of the email address. Grant access to the view instead of the table.
C. Create a masking policy that uses 'CASE statement based on IS_ROLE_IN_SESSION() function. When the role is Analyst role it should display original value, if not then it must display null value. Apply this policy to the EMAIL ADDRESS column in the CUSTOMERS table.
D. Use the 'SHA2()' function with a salt to hash the email addresses. Authorized users can reverse the hash using the correct salt.
E. Create a masking policy using the function to redact portions of the email address. Apply this policy to the EMAIL_ADDRESS column in the CUSTOMERS table.

Answer: C

Explanation:
Option C provides the best combination of security and flexibility. Snowflake's masking policies, combined with functions like , allow for dynamic masking based on user roles. Authorized users (e.g., those in an 'analyst' role) can see the original email address, while others see a masked version. Options A and D are not recommended as hashing/encryption functions like SHA2() and MD5() are not easily reversible and are not the intended use case for data masking policies. Option B offers masking with REGEXP_REPLACE but no mechanism to unmask with proper permissions. Option E creates a view, which is not centrally managed or scalable solution.

NEW QUESTION # 13
When maintaining reports and dashboards, why is it crucial to build automated and repeatable tasks?

A. Repeatable tasks hinder data updates in dashboards.
B. Automated tasks reduce manual efforts, ensuring consistency.
C. They ensure inconsistency in reports and dashboards.
D. Automated tasks increase the complexity of dashboard management.

Answer: B

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.

NEW QUESTION # 14
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 INFER_SCHEMA together with the CREATE TABLE USING TEMPLATE command.
D. Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE USING TEMPLATE command.

Answer: C

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 # 15
In Snowflake, how do window functions differ from table functions?

A. Window functions work on entire tables.
B. Table functions return tables as results.
C. Table functions operate on windowed rows.
D. Window functions modify table structures.

Answer: C

Explanation:
Table functions operate on windowed rows, distinguishing them from window functions in Snowflake.

NEW QUESTION # 16
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