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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q11-Q16):

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

Antwort: D

Begründung:
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.

12. Frage
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?

A. Presenting data visually increases complexity in analysis.
B. They enhance data comprehension, aiding effective analysis.
C. Visualizations don't impact data comprehension or analysis significantly.
D. Visualizations limit data exploration and analysis capabilities.

Antwort: B

Begründung:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.

13. Frage
When performing forecasting using statistics and built-in functions, what role do these functions play?

A. These functions only apply to limited data types for forecasting.
B. They enable the creation of custom forecasting models as needed.
C. They solely aid in data visualization without impacting the forecasting process.
D. They restrict forecasting to predefined models without flexibility.

Antwort: B

Begründung:
Built-in functions in statistics facilitate the creation of custom forecasting models, allowing flexibility in the forecasting process.

14. Frage
A marketing team wants to visualize website traffic data in Snowsight. They have a table named 'WEBSITE TRAFFIC' with columns 'VISIT DATE' (DATE), 'PAGE URL' (VARCHAR), and 'VISITOR COUNT' (INTEGER). The team wants to create a line chart showing the daily visitor count for a specific page, filtered by date range, allowing users to dynamically select the page URL from a dropdown. Which of the following steps are MOST effective and correct to achieve this using Snowsight dashboards?

A. Write a Python script using the Snowflake Connector to query the data, process it using Pandas, and then generate a chart using Matplotlib. Embed the chart as an image in the Snowsight dashboard.
B. Create a new chart in a Snowsight dashboard. Use a SQL query like 'SELECT VISIT_DATE, FROM WEBSITE_TRAFFIC WHERE PAGE URL = 'specific_page' AND VISIT DATE BETWEEN 'start_date' AND 'end_date' GROUP BY VISIT DATE ORDER BY VISIT DATE$. Manually update the query for each page.
C. Create a materialized view using 'CREATE MATERIALIZED VIEW AS SELECT VISIT_DATE, PAGE_URL, AS TOTAL VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT DATE, PAGE URL;'. In Snowsight, create a dashboard and a line chart using this materialized view with a filter on and configured as dashboard variables.
D. Create a stored procedure in Snowflake that accepts 'start_date', 'end_date' , and as parameters, executes the query, and returns the result set. Call the stored procedure from the Snowsight dashboard, passing the filter values.
E. Create a view 'DAILY PAGE_VISITS using a SQL query like 'CREATE OR REPLACE VIEW DAILY PAGE_VISITS AS SELECT VISIT DATE, PAGE URL, AS TOTAL_VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT_DATE, PAGE_URL;'. Then, in Snowsight, create a dashboard and a line chart using this view with a filter on 'PAGE URL' and 'VISIT DATE', configured as dashboard variables.

Antwort: C,E

Begründung:
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.

15. Frage
When handling Parquet files in Snowflake, what limitations or challenges might arise? (Select all that apply)

A. Difficulties in accessing specific file types
B. Restrictions in metadata retrieval
C. Issues in handling large Parquet files
D. Constraints in querying nested Parquet structures

Antwort: C,D

Begründung:
Challenges include handling large Parquet files and querying nested Parquet structures, which might pose limitations or complexities when working with Parquet files in Snowflake.

16. Frage
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