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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. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions
Topic 2: 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. Common Table Expressions (CTEs) 2. Window functions 3. PIVOT/UNPIVOT operations 4. QUALIFY clauses
Topic 3: Data Ingestion and Data Preparation	15%-20%	- 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 - 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) - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from structured sources (CSV) 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Retrieve data from unstructured sources - Implement data processing solutions 1. Use logging and monitoring solutions (auditing, data lineage) 2. Respond to processing failures 3. Automate and implement data pipelines (scheduling) 4. Cleanse, conform, and enrich data - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Create tables and views 2. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 3. Find external data sets that correlate with available data
Topic 4: Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Snowsight dashboards 2. Present analytical results - Integrate with BI tools 1. Tableau integration 2. Other partner visualization tools 3. Power BI integration

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

NEW QUESTION # 26
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?

A. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
B. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
C. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
D. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.
E. Optimize the Snowpipe configuration by adjusting the 'COPY INTO' statement to use file format options appropriate for the source data (e.g., compression, field delimiters).

Answer: C

Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.

NEW QUESTION # 27
A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?

A. select avg(total_income), state from taxpayer_wages group by state having state = @state;
B. select avg(total_income), state from taxpayer_wages group by state having state = state;
C. select avg(total_income), state from taxpayer_wages group by state having state = :state;
D. select avg(total_income), state from taxpayer_wages group by state having state = 'state';

Answer: C

Explanation:
In Snowsight, Snowflake's modern web interface, Filters are used to make dashboards and worksheets interactive. When a Data Analyst creates a custom filter, they define a "SQL Keyword" that acts as a variable within their SQL code. This allows users to select a value from a dropdown or date picker in the UI, which then dynamically updates the query's results.
The standard syntax for referencing a filter variable in a Snowflake worksheet or dashboard is the colon prefix (:) followed by the keyword name. In this specific scenario, the keyword is state. Therefore, the query must use :state to tell Snowflake that this value should be supplied by the dashboard's filter UI rather than being a literal string or a column reference.
Evaluating the Options:
* Option A is the 100% correct syntax. It correctly uses the :state identifier to link the query logic to the dashboard filter.
* Option B is incorrect because the @ symbol is typically used in Snowflake to reference stages (e.g.,
@mystage), not dashboard filters.
* Option C is incorrect because state = state is a tautology that will always evaluate to true for non-null values; it does not reference an external filter.
* Option D is incorrect because 'state' is a string literal. The query would literally look for a state named
"state" rather than using the user's selection.
Using these filters effectively is a core requirement for the Data Presentation and Data Visualization domain, as it enables the creation of self-service analytics tools where non-technical users can explore data subsets without modifying SQL code.

NEW QUESTION # 28
You are working with a table named 'PRODUCT DETAILS' that contains a 'PRICE column stored as a VARCHAR. The data in this column has inconsistencies, including leading/trailing spaces, currency symbols (e.g., '$', 'O'), and different decimal separators ('.' And ','). Additionally, some values are represented as 'N/A' or an empty string. You need to clean and validate this data to ensure the "PRICE' column can be safely converted to a NUMERIC data type. Choose the set of SQL transformations that will correctly clean the PRICE column. (Select all that apply)

A. ```sql UPDATE PRODUCT_DETAILS SET PRICE = TRIM(PRICE);```
B. UPDATE PRODUCT_DETAILS SET PRICE = NULL WHERE PRICE IN ('N/A',");```
C. ```UPDATE PRODUCT_DETAILS SET PRICE = REPLACE(PRICE,',','.' );```
D. ```sql UPDATE PRODUCT DETAILS SET PRICE = '[$, ]';```
E. ```sql ALTER TABLE PRODUCT DETAILS ALTER COLUMN PRICE SET DATA TYPE NUMBER (10,2);```

Answer: A,B,C,D

Explanation:
This question requires multiple correct answers. Options A, B, C, and D are all necessary for cleaning the 'PRICE' column. Option A handles the 'N/A' and empty string values by setting them to NULL. Option B removes leading/trailing spaces using the 'TRIM' function. Option C removes currency symbols using 'REGEXP REPLACES. Option D standardizes the decimal separator by replacing commas with periods using REPLACE. Option E is not correct because it attempts to change the data type of the 'PRICE column, but the column is not yet cleaned to allow for successful conversion to a NUMERIC data type.

NEW QUESTION # 29
In Snowsight, why is the utilization of different chart types crucial for effective data analysis and interpretation?

A. Snowsight doesn't support multiple chart types.
B. Different chart types offer diverse data representation.
C. Multiple chart types in Snowsight hinder data interpretation.
D. It limits users' ability to visualize data accurately.

Answer: B

Explanation:
Different chart types offer varied data representation, aiding analysis and interpretation in Snowsight.

NEW QUESTION # 30
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?

A. Website clickstream data stored in AWS S3 buckets in Parquet format.
B. Social media activity data ingested via a third-party API and stored in a relational database.
C. Database containing HR employee data.
D. IoT sensor data containing temperature readings.
E. Salesforce data containing customer interactions and sales opportunities.

Answer: A,B,E

Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.

NEW QUESTION # 31
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