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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion and Data Preparation	15%-20%	- Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from structured sources (CSV) 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Synthetic Data Generation - 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. Identify elements required for business goals using BI reports or SQL analysis 4. Determine the level of data granularity required 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. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 3. Create tables and views - 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. Define primary keys for tables 2. Implement constraints 3. Perform table joins between parent/child tables - Implement data processing solutions 1. Automate and implement data pipelines (scheduling) 2. Cleanse, conform, and enrich data 3. Use logging and monitoring solutions (auditing, data lineage) 4. Respond to processing failures
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Snowflake-specific analytical features 3. Aggregate functions
Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Snowsight dashboards 2. Present analytical results - Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools
Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. QUALIFY clauses 2. Common Table Expressions (CTEs) 3. Window functions 4. PIVOT/UNPIVOT operations - Design data models 1. Data vault models 2. Snowflake schema design 3. Star schema design

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
Consider a scenario where you're analyzing website user behavior data in Snowflake. You have a table named 'user sessionS with a column containing semi-structured data (VARIANT type) describing user interactions during a session. You need to create a UDF that accepts and extracts all the distinct event types that occurred within that session. The UDF should return an array of unique event type strings. This array will be used later to identify users who have participated in a specific combination of events. Which of the following approaches can effectively achieve this using Snowflake's SQL extensibility features?

A. All of the above can satisfy the question. (Single Correct Answer)
B. Using an external function with a remote service to process 'session_data' and return JSON array with distinct event types. The external function setup must include API integration with the third party service and Role based access control. (Single Correct Answer)
C. Implementing a Java UDF that parses the session data, extracts the event types, uses Java's HashSet to guarantee uniqueness, and returns the HashSet as a comma-separated string. (Single Correct Answer)
D. Using a JavaScript UDF that iterates through the session data, extracts the event types, adds them to a JavaScript Set to ensure uniqueness, and then converts the Set to an array. This array is returned as a result. (Single Correct Answer)
E. Creating a SQL UDF that utilizes Snowflake's ARRAY AGG and DISTINCT functions to aggregate all event types from the session data into an array, ensuring uniqueness. (Single Correct Answer)

Answer: D

Explanation:
The most efficient and appropriate solution is A. A JavaScript UDF allows for direct manipulation of the session data (VARIANT) and leveraging JavaScript's Set object for efficient uniqueness enforcement before returning the result as an array. This minimizes data transformation overhead within Snowflake's SQL engine. B: While SQL UDFs are an option, processing nested data and enforcing uniqueness within SQL can be less efficient compared to JavaScript's built-in capabilities. C: Using java, while possible, add overhead to the processing since Java UDF requires to setup class definitions and imports which can be an overkill for this use case. D: This is an external API integration which has extra overhead and latency. E: Is incorrect because all approaches have tradeoffs and can be implemented in certain instances based on requirements.

NEW QUESTION # 63
A healthcare provider is investigating patient readmission rates within 30 days of discharge. They suspect a correlation between patient demographics (age, gender, location) and readmission. You have the following tables: 'PATIENTS': 'patient_id', 'age', 'gender' , 'zip_code' 'ADMISSIONS': 'admission_id' , 'patient_id' , 'admission_date', 'discharge_date' Which of the following approaches would be MOST effective to identify patient demographics significantly correlated with higher readmission rates within 30 days? (Select TWO)

A. Create a Snowflake user-defined function (UDF) in Python to perform a complex machine learning model directly on the data to predict readmissions based on demographics, without any initial exploratory data analysis.
B. Develop a complex SQL query to directly identify and list all patients who have been readmitted more than twice in the last year, irrespective of their demographics.
C. Implement a cohort analysis to track patient readmission rates over time for different demographic segments and visualize the trends using Snowflake's data visualization capabilities (if integrated) or export the data to a BI tool.
D. Use correlation coefficients (e.g., Pearson, Spearman) to directly measure the linear association between demographics and the binary readmission outcome (1 -readmitted, 0=not readmitted).
E. Calculate the overall readmission rate and compare it to the readmission rates for different demographic groups using Chi-Square tests or similar statistical methods to assess statistical significance.

Answer: C,E

Explanation:
Options A and D are the most effective. A suggests using statistical tests (Chi-square) which can identify statistically significant differences in readmission rates across demographic groups. D proposes cohort analysis, enabling the tracking and visualization of readmission trends for different demographics over time, allowing for the identification of segments with consistently high readmission rates. Option B is not optimal as it jumps directly into complex modeling without initial EDA. Option C, while valid, doesn't handle categorical variables (gender, zip_code) well and might miss non-linear relationships. Option E, identifying high-frequency readmitters, is helpful but doesn't directly address the relationship with demographics.

NEW QUESTION # 64
Which of the following statements regarding Secure Views and Materialized Views in Snowflake are CORRECT? (Choose two)

A. Both Secure Views and Materialized Views can be automatically refreshed based on data changes in the underlying tables.
B. Secure Views incur a performance overhead compared to standard views, while Materialized Views generally improve query performance for suitable workloads.
C. Secure Views can be used in conjunction with Row Access Policies to control data visibility at a granular level. Materialized views cannot be used in conjunction with row access policies.
D. Materialized Views can only be created on tables, not on other views or functions.
E. Secure Views obfuscate the underlying query logic from users, while Materialized Views store pre-computed data for faster query performance.

Answer: B,E

Explanation:
Secure Views hide query logic and data lineage, improving security. Materialized Views pre-compute and store data, improving performance. Secure Views can have a performance overhead as additional checks are performed. Materialized Views are automatically refreshed in Snowflake. Materialized views cannot be created with User-Defined Functions in the Select List or Where Clause. Row access policies are not supported on materialized views.

NEW QUESTION # 65
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

A. They enable the execution of repetitive tasks, enhancing efficiency.
B. UDFs enhance query performance more effectively than stored procedures.
C. Stored procedures allow limited data accessibility for improved security.
D. Stored procedures hinder customization in data operations.

Answer: A

Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.

NEW QUESTION # 66
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. 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.
B. 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.
C. 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.
D. 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.
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.

Answer: D,E

Explanation:
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.

NEW QUESTION # 67
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