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Snowflake DAA-C01 Exam Syllabus Topics:

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

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

NEW QUESTION # 26
This command was executed:
SQL
SELECT seq4(), uniform(1, 10, RANDOM(12))
FROM TABLE(GENERATOR(TIMELIMIT => NULL))
ORDER BY 1;
How many rows will be generated?

A. An infinite number
B. 0
C. 1
D. 2

Answer: D

Explanation:
The GENERATOR table function is used in Snowflake to produce a synthetic result set, typically for testing or creating dummy data. However, the GENERATOR function requires at least one of two parameters to be explicitly set to a positive value to produce any rows: ROWCOUNT or TIMELIMIT.
In the provided query, the user has set TIMELIMIT => NULL. In Snowflake's SQL implementation for the GENERATOR function, NULL is treated as a zero or an undefined limit. Furthermore, since the ROWCOUNT parameter is omitted, it defaults to zero. Because neither a positive duration nor a positive row count has been specified, the generator engine has no instruction on how many rows to build.
Evaluating the Results:
* Option A is a distractor; while one might think NULL means "no limit" (infinity), the GENERATOR function is designed to be finite and safe.
* Option B is incorrect; the number 12 inside the RANDOM(12) function is merely a seed value for the random number generator, ensuring the output is reproducible. It has no impact on the number of rows produced by the FROM clause.
* Option C is incorrect; 10 is just a boundary for the UNIFORM function.
* Option D is the 100% correct answer. Because the query specifies TIMELIMIT => NULL and fails to provide a ROWCOUNT, Snowflake will execute the query successfully but return a result set containing 0 rows. This is a common "trick" question in the Data Analysis domain to test an analyst's familiarity with table function parameters.

NEW QUESTION # 27
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.
C.
D.
E. None of the above.

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 # 28
When building a Snowsight dashboard that will allow users to filter data within a worksheet, which Snowflake system filters should be used?

A. Include the :datebucket system filter in a WHERE clause, and include the :daterange system filter in a GROUP BY clause.
B. Include the :daterange system filter in a WHERE clause, and include the :datebucket system filter in a GROUP BY clause.
C. Include the :daterange system filter in a SELECT clause, and include the :datebucket system filter in a GROUP BY clause.
D. Include the :datebucket system filter in a WHERE clause, and include the :daterange system filter in a SELECT clause.

Answer: B

Explanation:
Snowsight provides special System Keywords that allow analysts to create dynamic, interactive dashboards without hard-coding dates. Two of the most critical keywords are :daterange and :datebucket.
The :daterange keyword is designed to filter the volume of data based on a time period selected by the user in the dashboard UI (e.g., "Last 30 days" or "Current Year"). Because it acts as a filter on the underlying data, it must be placed in the WHERE clause of the SQL statement (e.g., WHERE created_at = :daterange). This ensures that only the records within the selected timeframe are processed by the query.
The :datebucket keyword is used for time-series aggregation. It allows the user to dynamically change the granularity of the data-for example, switching a chart from "Daily" to "Monthly" views without rewriting the query. To achieve this, :datebucket is used inside a date-truncation function in the SELECT list and, crucially, must be included in the GROUP BY clause to correctly aggregate the metrics (e.g., GROUP BY 1 or GROUP BY :datebucket).
Evaluating the Options:
* Option A is incorrect because :datebucket is for grouping/truncation, not for filtering in a WHERE clause.
* Option B is incorrect because :daterange is a filter (returning a range), not a scalar value suitable for a SELECT list.
* Option C is incorrect for the same reasons as A and B.
* Option D is the 100% correct answer. It follows the standard Snowsight design pattern: :daterange restricts the data rows in the WHERE clause, while :datebucket defines the temporal aggregation level in the GROUP BY clause.

NEW QUESTION # 29
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. 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.
B. 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.
C. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.
D. 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.
E. 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.

Answer: E

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 # 30
Which of the following statements regarding Secure Views and Materialized Views in Snowflake are CORRECT? (Choose two)

A. Secure Views obfuscate the underlying query logic from users, while Materialized Views store pre-computed data for faster query performance.
B. Materialized Views can only be created on tables, not on other views or functions.
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. Secure Views incur a performance overhead compared to standard views, while Materialized Views generally improve query performance for suitable workloads.
E. Both Secure Views and Materialized Views can be automatically refreshed based on data changes in the underlying tables.

Answer: A,D

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 # 31
......

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