2026 Useful DAA-C01: SnowPro Advanced: Data Analyst Certification Exam Online Version

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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%	- 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 - Perform data discovery to identify what is needed from available datasets 1. Query tables to assess data elements and statistics maintained by Snowflake 2. Identify elements required for business goals using BI reports or SQL analysis 3. Determine the level of data granularity required 4. Evaluate required transformations (table joins, set operations, ASOF JOINS) 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. Create tables and views 2. Find external data sets that correlate with available data 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) - Implement data processing solutions 1. Automate and implement data pipelines (scheduling) 2. Use logging and monitoring solutions (auditing, data lineage) 3. Respond to processing failures 4. Cleanse, conform, and enrich data - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Implement constraints 3. Define primary keys for tables
Topic 3: Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Topic 4: Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Snowflake schema design 3. Star schema design - Transform data using SQL 1. PIVOT/UNPIVOT operations 2. QUALIFY clauses 3. Window functions 4. Common Table Expressions (CTEs)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
A Snowflake table 'USER ACTIVITY' contains a column 'ACTIVITY TIMESTAMP' of type VARCHAR, which stores timestamps in different formats, including ISO 8601 (e.g., '2023-10-27 Unix epoch seconds (e.g., '1698400800'), and milliseconds (e.g., '1698400800000'). You need to convert all these timestamps into a consistent TIMESTAMP NTZ data type in Snowflake. Which approach offers the most efficient and accurate solution for converting these diverse timestamp formats into a unified TIMESTAMP NTZ?

A. Create separate temporary tables for each timestamp format using 'TO_TIMESTAMP' , then UNION ALL them together.
B. First convert epoch milliseconds to epoch seconds by dividing by 1000. Then use a single 'TO TIMESTAMP' function assuming default ISO 8601 format.
C. Use a series of 'CASE' statements with ' TO_TIMESTAMP' and multiple format strings to parse each format individually.
D. Utilize Snowflake's automatic type conversion capabilities by simply changing the column's data type to TIMESTAMP_NTZ using ALTER TABL
E. Use a UDF (User-Defined Function) that intelligently detects the timestamp format and applies the appropriate conversion function CTO TIMESTAMP , etc.).

Answer: E

Explanation:
Option C provides the most efficient and accurate solution. While 'CASE' statements (Option A) are viable, they can become complex and less maintainable with many formats. Creating temporary tables (Option B) is inefficient. Dividing milliseconds and assuming ISO 8601 (Option D) will fail for true ISO 8601 formats and might cause loss of precision. Snowflake's automatic type conversion (Option E) won't handle the mixed formats. A UDF (Option C) encapsulates the logic for format detection and conversion, promoting code reusability and maintainability. It allows you to use different Snowflake functions within the UDF to parse different formats, ensuring accurate conversion to a unified TIMESTAMP NTZ data type.

NEW QUESTION # 27
You are working with a Snowflake table 'raw_data' containing a column of type TEXT that stores log messages in various formats, including JSON and CSV. You need to extract specific data points from these log entries, treating JSON entries differently from CSV entries. Specifically: For JSON log entries (identified by starting with '{l), extract the value of the 'user_id' key. For CSV log entries (identified by starting with a number), extract the second field (assuming comma-separated values). Which of the following queries is the most efficient and correct way to achieve this data extraction?

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

Answer: C

Explanation:
Option B provides the most efficient and correct solution. 1, 1) = '{' ' efficiently checks if the log entry starts with '{' without needing the full power of STARTS_WITH or PARSE_JSON. For JSON entries, it correctly uses to extract the 'user_id' directly from the VARIANT representation. For CSV entries, it uses ',', 2)' which is specifically designed to extract a single part from a delimited string, and is more efficient than using SPLIT. Option A will not treat the JSON objects in the 'log_entry' column as valid variants and needs to be parsed using PARSE_JSON explicitly to get correct answer. Option C uses GET _ PATH which cannot be used without parsing the JSON first. Option D will error out because you cannot use JSON dot notation on a TEXT column directly. Option E uses SPLIT TO TABLE which is more costly than SPLIT PART.

NEW QUESTION # 28
Why is the Parquet format preferred for complex data sets?

A. It supports efficient compression and encoding schemes
B. It has visually appealing data presentation
C. It randomly alters data for testing
D. It changes data colors for differentiation

Answer: A

NEW QUESTION # 29
You are tasked with validating the data in a 'SALES table. The table contains 'ORDER ID', 'CUSTOMER ID', 'PRODUCT ID, 'SALE DATE, and 'SALE AMOUNT'. You need to identify orders where the 'SALE AMOUNT is negative or zero, indicating potential data entry errors, and also find orders with duplicate 'ORDER ID' values but different 'SALE AMOUNT values. Which of the following queries, when combined, would efficiently achieve this validation?

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

Answer: B

Explanation:
The first query 'SELECT ORDER ID, SALE_AMOUNT FROM SALES WHERE SALE AMOUNT O;' identifies orders with non- positive sale amounts. The second query 'SELECT ORDER_ID, FROM SALES GROUP BY ORDER_ID HAVING COUNT(DISTINCT SALE_AMOUNT) > 1;' efficiently finds ORDER_lDs that have multiple distinct SALE_AMOUNT values, indicating duplicates with conflicting data. "ANY _ VALUE is used since we only care about the 'ORDER_ID'.

NEW QUESTION # 30
A Data Analyst is creating a Snowsight dashboard from a shared worksheet. What happens to the access and permissions of the users who initially had sharing privileges on the worksheet?

A. The original users retain access and permissions on the worksheet.
B. The original users gain additional access to the worksheet.
C. The original users lose access to the worksheet, their permissions on the worksheet are revoked.
D. The original users temporarily lose access but regain it once the dashboard is created.

Answer: C

Explanation:
When working within the Snowsight interface, the transition from a standalone worksheet to a dashboard component involves a change in how the underlying SQL and its associated metadata are managed. When a worksheet is converted or used to create a dashboard, the ownership and sharing model shifts to the dashboard level.
According to Snowflake's documentation on Snowsight collaboration, when a user creates a dashboard from a worksheet that was previously shared with others, the original worksheet's individual share settings are essentially superseded by the dashboard's own permissions. In many workflow scenarios within the UI, once the worksheet is finalized into a dashboard tile, the direct, independent access to that specific worksheet is severed for the original "sharees." Their permissions on that specific worksheet are revoked to prevent conflicting edits between the standalone version and the dashboard-integrated version.
Evaluating the Options:
* Option A is incorrect because Snowsight manages the lifecycle of worksheets used in dashboards as part of the dashboard object; independent sharing of the underlying worksheet is typically disabled or revoked.
* Option B and C are distractors; there is no mechanism in Snowsight that grants "additional" access or
"temporary" loss during the creation process.
* Option D is the 100% correct answer based on the SnowPro Data Analyst standard regarding Snowsight object ownership and sharing lifecycle. To allow others to see the work, the Analyst must now share the Dashboard itself, rather than relying on the previous worksheet-level permissions. This ensures a "single source of truth" for the visualization's logic.

NEW QUESTION # 31
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