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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. QUALIFY clauses 2. Window functions 3. Common Table Expressions (CTEs) 4. PIVOT/UNPIVOT operations
Topic 2: Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Other partner visualization tools 2. Tableau integration 3. Power BI integration - Create dashboards 1. Present analytical results 2. Snowsight dashboards
Topic 3: Data Ingestion and Data Preparation	15%-20%	- Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Retrieve data from structured sources (CSV) 4. Retrieve data from unstructured sources - Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Perform table joins between parent/child tables 3. Define primary keys for tables - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Find external data sets that correlate with available data 3. Create tables and views - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Implement data processing solutions 1. Use logging and monitoring solutions (auditing, data lineage) 2. Respond to processing failures 3. Cleanse, conform, and enrich data 4. Automate and implement data pipelines (scheduling) - Perform data discovery to identify what is needed from available datasets 1. Determine the level of data granularity required 2. Identify elements required for business goals using BI reports or SQL analysis 3. Evaluate required transformations (table joins, set operations, ASOF JOINS) 4. Query tables to assess data elements and statistics maintained by Snowflake 5. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Time-series analysis 3. Snowflake-specific analytical features

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

NEW QUESTION # 62
You are designing a data warehouse in Snowflake for a retail company. The company has two tables: 'Transactions' (TransactionlD, CustomerlD, ProductlD, TransactionDate, Amount) and 'Products' (ProductlD, ProductName, CategorylD, Price). The 'Transactions' table contains millions of rows, and analysts frequently run queries that join these tables to analyze sales by product category. To optimize query performance and reduce data redundancy for these analytical queries, which of the following strategies would be MOST effective, considering Snowflake's architecture and best practices?

A. Create a stored procedure that automatically rebuilds the standard view daily using the latest data from the 'Transactions' and 'Products' tables.
B. Create a standard view that joins the 'Transactions' and 'Products' tables, and rely on Snowflake's query optimizer to automatically optimize the join performance.
C. Create a clustered table that includes all columns in transactions and products tables using 'CLUSTER BY' clause to cluster most frequently used column.
D. Create a materialized view that joins the 'Transactions' and 'Products' tables and includes relevant columns for analysis, partitioning the view by 'TransactionDates.
E. Denormalize the 'Transactions' table by adding 'ProductName' and 'CategorylD columns directly to the 'Transactions' table using a data transformation pipeline after the initial load.

Answer: D,E

Explanation:
Options A and D are correct. Creating a materialized view (option A) is a good approach if the underlying tables don't change often. Snowflake will automatically refresh the materialized view when changes occur in the base tables. In this case, the company will have increased query performance. Partitioning by TransactionDate also improves the query's performance. Materialized views store the pre-computed results, significantly speeding up queries that use the view. Denormalizing (option D) by adding 'ProductName' and 'CategorylD' to the 'Transactions' table avoids the join entirely for queries that only need these columns, improving performance and reducing data redundancy. A standard view (option B) doesn't store pre-computed results. While Snowflake's optimizer is effective, it won't provide the same performance gains as a materialized view or denormalization. Option C is not correct because creating a clustered table does not avoid the need to join the tables. Option E is not correct because using the materialized view, the Snowflake will automatically refresh it when changes occur.

NEW QUESTION # 63
You are preparing data for a machine learning model that predicts customer churn. You have a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT'. Your model requires a feature representing the recency of the customer's last transaction (days since last transaction) calculated as of today. Which of the following SQL snippets in Snowflake best achieves this, considering potential performance implications on a large dataset?

Answer: E

Explanation:
The best approach is E because it explicitly casts 'CURRENT _ to a DATE data type using 'CAST(CURRENT This ensures consistency and avoids potential issues if the TRANSACTION DATE' column is also of DATE data type. provides a date only, while provides date and time. Using 'CURRENT directly in 'DATEDIFF with a DATE field could lead to unexpected results due to the time component. Also other options are incorrect because 'GETDATE()' does not exist in snowflake, and because is redundant since CURRENT TIMESTAMP returns a timestamp already

NEW QUESTION # 64
You have a Snowflake table named 'CUSTOMER DATA with a 'JOIN DATE column currently stored as VARCHAR. You need to convert this column to a DATE data type. However, the 'JOIN DATE column contains various date formats, including 'YYYY-MM-DD', 'MM/DD/YYYY', and some invalid date strings like 'UNKNOWN'. Which combination of Snowflake SQL functions and techniques provides the MOST robust solution to convert the column to a DATE data type while handling invalid values gracefully?

A. Use 'TO DATE(JOIN DATE)' and handle errors during the conversion process by correcting invalid date formats manually.
B. Create multiple temporary tables for each date format using 'TO DATE, then union them together, handling the conversion errors through separate logic.
C. Use 'CASE statements with 'TRY TO DATE and format strings to handle multiple date formats and assign a default value to rows that cannot be converted to a date.
D. Use 'YYYY-MM-DD')' along with error handling using 'BEGIN...EXCEPTION...END block to capture conversion errors and update the corresponding rows to a default date.
E. Use 'YYYY-MM-DD')' , then update any resulting NULL values with a default date like '1900-01-01

Answer: C

Explanation:
Option C offers the most robust solution. Using 'CASE' statements with 'TRY TO DATE' allows you to specify multiple format strings to handle the different date formats present in the 'JOIN_DATE column. The function gracefully handles invalid date strings by returning NULL, which can then be replaced with a default value using the 'ELSE clause in the 'CASE' statement. This approach avoids errors during conversion and ensures that all rows have a valid date value or a meaningful default.

NEW QUESTION # 65
When using Snowflake functions, how do scalar functions differ from aggregate functions?

A. Aggregate functions modify table structures.
B. Aggregate functions return single values.
C. Scalar functions work on groups of rows.
D. Scalar functions operate on entire tables.

Answer: B

Explanation:
Aggregate functions return single values from groups of rows, distinguishing them from scalar functions.

NEW QUESTION # 66
What role does operationalizing data play in maintaining reports and dashboards for business requirements?

A. Operationalizing data complicates dashboard management.
B. It restricts data accessibility, affecting dashboard usability.
C. It limits data updates, affecting dashboard accuracy.
D. Operationalizing data ensures consistent and efficient usage.

Answer: D

Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 67
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