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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. Tableau integration 2. Other partner visualization tools 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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 2. Identify elements required for business goals using BI reports or SQL analysis 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Evaluate required transformations (table joins, set operations, ASOF JOINS) 5. Determine the level of data granularity required - 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. Implement constraints 2. Perform table joins between parent/child tables 3. Define primary keys for tables - Use a collection system to retrieve data 1. Retrieve data from structured sources (CSV) 2. Retrieve data from unstructured sources 3. Synthetic Data Generation 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Create tables and views 3. Find external data sets that correlate with available data - Implement data processing solutions 1. Cleanse, conform, and enrich data 2. Automate and implement data pipelines (scheduling) 3. Use logging and monitoring solutions (auditing, data lineage) 4. Respond to processing failures
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Time-series analysis 3. Snowflake-specific analytical features
Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design - Transform data using SQL 1. QUALIFY clauses 2. Window functions 3. PIVOT/UNPIVOT operations 4. Common Table Expressions (CTEs)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)

A. Volume of data
B. Business requirements
C. Data source availability
D. Data collection tool limitations

Answer: A,B

Explanation:
Assessing data collection frequency involves considering business requirements and the volume of data necessary for analysis.

NEW QUESTION # 59
A Snowflake data warehouse contains a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID, 'TRANSACTION DATE', 'AMOUNT', and 'PRODUCT CATEGORY'. Analysts frequently run queries that aggregate transaction amounts by product category for specific customer segments. The following query pattern is common:

Which of the following strategies, when implemented together, would BEST optimize the performance of this query pattern, considering both result caching and data access patterns?

A. create a materialized view that pre-aggregates 'SUM(AMOUNT)' by 'PRODUCT_CATEGORY, 'CUSTOMER_ID , and 'TRANSACTION_DATE. Regularly refresh the materialized view.
B. Cluster the 'CUSTOMER TRANSACTIONS' table by 'CUSTOMER and then 'TRANSACTION DATE. Create a materialized view preaggregating 'SUM(AMOUNT)' by PRODUCT_CATEGORY', and ' TRANSACTION_DATE
C. Create a view that encapsulates the 'WHERE' clause conditions (filtering by 'CUSTOMER ID and 'TRANSACTION DATE). Enable automatic query rewrite.
D. Tune the virtual warehouse size to be as small as possible while still meeting performance requirements. Ensure the statistics on the table are up to date.
E. Create indexes on 'CUSTOMER and 'TRANSACTION DATE columns of the CUSTOMER TRANSACTIONS table.

Answer: B

Explanation:
Clustering 'CUSTOMER TRANSACTIONS' by 'CUSTOMER and 'TRANSACTION DATE improves data access performance by organizing data physically based on the frequently used filter criteria. Creating a materialized view that pre-aggregates the results by PRODUCT CATEGORY, 'CUSTOMER ID', and 'TRANSACTION DATE' allows Snowflake to serve the results directly from the materialized view, significantly reducing the compute cost and improving query performance. Snowflake does not use traditional indexes; clustering provides similar benefits.

NEW QUESTION # 60
You are designing a data ingestion pipeline for a financial institution. The pipeline loads transaction data from various sources into a Snowflake table named 'TRANSACTIONS. The 'TRANSACTIONS table includes columns such as TRANSACTION , 'ACCOUNT ID', 'TRANSACTION DATE, 'TRANSACTION AMOUNT, and 'TRANSACTION TYPE. The data is loaded in micro- batches using Snowpipe. Due to potential source system errors and network issues, duplicate records with the same 'TRANSACTION ID' are occasionally ingested. You need to ensure data integrity by preventing duplicate 'TRANSACTION_ID' values in the 'TRANSACTIONS' table while minimizing the impact on ingestion performance. Which of the following approaches is the MOST efficient and reliable way to handle this deduplication requirement in Snowflake, considering data integrity and performance?

A. Create a scheduled task that runs every hour to identify and delete duplicate records based on 'TRANSACTION ID. The task will use a SQL query to find duplicate ' TRANSACTION ID values and remove the older entries.
B. Create a staging table with the same schema as 'TRANSACTIONS'. Use a 'MERGE' statement within the Snowpipe load process to insert new records from the incoming data into the 'TRANSACTIONS' table, only if the 'TRANSACTION ID does not already exist. Define 'TRANSACTION ID' as the primary key in the staging table. Use clustering on 'TRANSACTION_ID on the target 'TRANSACTIONS' table.
C. Create a stream on the 'TRANSACTIONS' table and use it to identify newly inserted rows. Then, use a merge statement to insert new, distinct transactions into a separate staging table. Finally, periodically truncate the original 'TRANSACTIONS table and load the deduped data from the staging table.
D. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.
E. Use a materialized view built on top of the TRANSACTIONS table that selects distinct transaction ids. This ensures that querying through the materialized view returns no duplicates.

Answer: B

Explanation:
Option E provides the most performant and robust solution. Although Snowflake doesn't enforce primary key constraints, defining them on the staging table and leveraging a 'MERGE' statement during the Snowpipe load process allows for efficient deduplication. Clustering on TRANSACTION_I[Y on the target table also helps with performance. A regular task would be less efficient and introduce latency. Snowflake does not automatically reject duplicate inserts based on defined primary keys (option A). Materialized views don't prevent duplicate data from entering the base table. Option C is possible but more complex to implement than a MERGE statement.

NEW QUESTION # 61
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: C

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 # 62
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?

A. SELECT user_name, SUM(credits_used_compute) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
B. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
C. SELECT user_name, SUM(credits_used_cloud_services) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time >=DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
D. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.execution_history WHERE start_time >= DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
E. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.warehouse_metering_history WHERE start_time >= DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;

Answer: A

Explanation:
The correct query should use the 'snowflake.account_usage.query_history' view and specifically sum the column as this reflects the credits used for the compute resources by query. Option C accurately reflects this. Option A is incorrect because the 'credits_used' column in 'query_history' does not give a direct credit consumption cost based on computation. Option B uses 'execution_history' which does not aggregate the same detailed credit usage information. Option D sums credits used for cloud services not the compute. Option E references warehouse metering history, not specific users' query execution history.

NEW QUESTION # 63
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