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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
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. Snowsight dashboards 2. Present analytical results
Data Ingestion and Data Preparation	15%-20%	- Perform data discovery to identify what is needed from available datasets 1. Query tables to assess data elements and statistics maintained by Snowflake 2. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 3. Evaluate required transformations (table joins, set operations, ASOF JOINS) 4. Determine the level of data granularity required 5. Identify elements required for business goals using BI reports or SQL analysis - Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Define primary keys for tables 3. Perform table joins between parent/child tables - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Synthetic Data Generation 4. Retrieve data from structured sources (CSV) - 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 - Implement data processing solutions 1. Automate and implement data pipelines (scheduling) 2. Respond to processing failures 3. Use logging and monitoring solutions (auditing, data lineage) 4. Cleanse, conform, and enrich data
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
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
How can automated and repeatable tasks contribute to maintaining reports and dashboards in meeting business requirements?

A. Automated tasks ensure consistency and reduce manual effort.
B. They solely increase the complexity of dashboard management.
C. They limit the scalability of dashboards and reports.
D. Repeatable tasks hinder data updates in dashboards.

Answer: A

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.

NEW QUESTION # 53
You are tasked with creating a stored procedure in Snowflake to perform data cleansing on a table named 'CUSTOMER DATA'. The procedure should: 1) Remove rows where the 'EMAIL' column is NULL or empty. 2) Standardize the 'PHONE NUMBER' column by removing all non-numeric characters and ensuring it's exactly 10 digits long. 3) Return the number of rows removed due to invalid emails and the number of rows modified due to phone number standardization. Assume the table already exists and contains columns 'CUSTOMER (INT), (VARCHAR), and 'PHONE NUMBER (VARCHAR). Which of the following code snippets correctly implements this stored procedure? The procedure should use exception handling to gracefully handle errors, returning -1 for both counts if any error occurs.

A.
B. None of the above.
C.
D.
E.

Answer: C

Explanation:
Option A is correct because it uses SQL to perform the data cleansing tasks, correctly utilizes 'SQLROWCOUNT' to capture the number of affected rows, and returns the results as a VARIANT OBJECT. It also includes proper exception handling. Options B, C, and D have errors in syntax or logic regarding return types, variable declaration, or how to retrieve row counts. Specifically, using Javascript or returning an ARRAY/TABLE when VARIANT is more flexible in this scenario.

NEW QUESTION # 54
What factors should be considered when evaluating which transformations are required in data discovery? (Select all that apply)

A. Data redundancy
B. Business use cases
C. Data consistency
D. Data normalization

Answer: B,C,D

Explanation:
Evaluating necessary transformations involves considering data consistency, normalization, and alignment with business use cases.

NEW QUESTION # 55
You are tasked with cleaning a 'customer_orders' table in Snowflake. The table contains columns like 'order_id', 'customer_id', 'order_date', and 'order_amount'. You notice that some 'order_amount' values are negative (representing returns), but you need to analyze total sales. Additionally, some 'order_date' values are in the future. Which of the following SQL transformations would BEST address these data quality issues to ensure accurate sales analysis?

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

Answer: E

Explanation:
Option C correctly addresses both issues. ensures that all amounts are positive, effectively treating returns as positive contributions to sales for the purpose of this specific analysis. 'order_date <= filters out future dates. Options A and D also exclude negative order amounts which is incorrect, whereas options B and E do not make all amounts positive for sales amount. The use of CTE is also correct. The question is testing the candidate on how to select the best transformations for given data challenges.

NEW QUESTION # 56
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?

A. Create a stored procedure that iterates through the 'CUSTOMER_DATA' table, retrieves demographic and credit risk information for each customer, applies the credit risk threshold, and inserts the enriched data into a new table.
B. Create a task that periodically runs a query that joins 'CUSTOMER DATA with both shared views (CUSTOMER ENRICHMENT from Share A and Share B), filtering based on the credit risk threshold. Insert the results into a new enriched table.
C. Create two separate views, one for each data share, and then join them based on in a final view, filtering for the credit risk threshold in the final view.
D. Create a single view that joins 'CUSTOMER_DATA' with both shared views ('CUSTOMER_ENRICHMENT from Share A and Share B) using a common table expression (CTE) to filter records from Share B to include only customers with a credit risk score above the defined threshold.
E. Replicate data from both data shares and perform enrichment and credit risk filtering on the replicated data.

Answer: D

Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.

NEW QUESTION # 57
......

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