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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. QUALIFY clauses 2. PIVOT/UNPIVOT operations 3. Window functions 4. Common Table Expressions (CTEs) - Design data models 1. Data vault models 2. Snowflake schema design 3. Star schema design
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 Ingestion and Data Preparation	15%-20%	- Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from unstructured sources 3. Retrieve data from structured sources (CSV) 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Implement constraints 3. Define primary keys for tables - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Create tables and views 2. Find external data sets that correlate with available data 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) - Implement data processing solutions 1. Respond to processing failures 2. Use logging and monitoring solutions (auditing, data lineage) 3. Automate and implement data pipelines (scheduling) 4. Cleanse, conform, and enrich data - Perform data discovery to identify what is needed from available datasets 1. Query tables to assess data elements and statistics maintained by Snowflake 2. Evaluate required transformations (table joins, set operations, ASOF JOINS) 3. Determine the level of data granularity required 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 5. Identify elements required for business goals using BI reports or SQL analysis - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Snowflake-specific analytical features 3. Time-series analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
A Data Analyst needs a sample of 10 rows from a table FCT_SALES that has billions of rows. Which commands can be used to accomplish this? (Select TWO).

A. SELECT TOP 10 * FROM FCT_SALES;
B. SELECT TOP (10 ROWS) * FROM FCT_SALES;
C. SELECT * FROM FCT_SALES SAMPLE (10 ROWS);
D. SELECT TOP 10 ROWS * FROM FCT_SALES;
E. SELECT * FROM FCT_SALES SAMPLE 10;

Answer: A,C

Explanation:
When dealing with massive datasets containing billions of rows, efficiency is paramount. Snowflake provides two primary ways to retrieve a small subset of data: Sampling and Result Set Limiting.
The SAMPLE clause is used to return a subset of rows based on a specific count or percentage. The correct syntax for specifying a fixed number of rows is SAMPLE (<number> ROWS). This is highly effective for exploratory data analysis where you need to see the shape of the data without the overhead of scanning the entire table. It is important to note that SAMPLE does not guarantee a truly random distribution unless specified, but it is much faster than a full table scan.
The TOP <n> or LIMIT <n> clauses are used to restrict the number of rows returned in the output. The TOP
<n> syntax is commonly used in many SQL dialects and is fully supported in Snowflake to return the first "n" rows the query engine encounters. While TOP is often used with an ORDER BY clause to get the highest or lowest values, using it alone on a massive table like FCT_SALES is a standard way to quickly peak at the data.
Evaluating the Options:
* Option A is incorrect syntax; the ROWS keyword and parentheses are required for a fixed count.
* Option B is Correct; it uses the standard Snowflake SAMPLE syntax for a specific row count.
* Option C and Option E are incorrect because ROWS is not a valid keyword within a TOP clause in Snowflake.
* Option D is Correct; it uses the standard TOP <n> syntax to limit the result set.
By using these methods, a Data Analyst ensures they are not wasting compute resources (Credits) by pulling billions of unnecessary rows into the UI or a local environment.

NEW QUESTION # 49
You have a Snowflake table called 'CUSTOMER ORDERS that stores customer order data'. The business requires you to generate a weekly report on the top 10 customers by order value, delivered as an Excel file to a shared network drive. The network drive is accessible by a service account that your Snowflake account can authenticate against. The report must include customer name, total order value, and number of orders. Which approach is the MOST secure and efficient for automating this process?

A. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format the data using Javascript UDF to XML, write the Excel file to an internal stage using Java UDF, and then use a Python UDF to copy the file to the network drive. Grant necessary permissions to the task's service account.
B. Use a Snowflake Task to trigger a Snowpipe. A Snowflake stored procedure that executes SQL code to query for relevant data, convert it to JSON, then the Snowpipe load into the network directory using REST API. Grant necessary permissions to the task's service account.
C. Create a Snowflake external function using AWS API Gateway and AWS Lambda. The external function queries the data from Snowflake, formats it as an Excel file using a Python library (e.g., openpyxl) within the Lambda function, and saves the file directly to the network drive using the service account's credentials. Configure API Gateway to authenticate requests from Snowflake.
D. Leverage a Snowflake Task to run a stored procedure. The procedure queries the data, transforms it into CSV format using Snowflake scripting. Then uses a Java UDF to copy the CSV to an internal stage, from where a separate process (outside Snowflake) monitors for new files and transfers them to the network drive using the service account. Securely manage credentials for both the Java UDF and the external process.
E. Create a view on top of the CUSTOMER_ORDERS table that calculates the required metrics. Use a third-party ETL tool to extract the data from the view, format it as an Excel file, and save it to the network drive. Configure the ETL tool with appropriate Snowflake credentials.

Answer: D

Explanation:
Option E provides a balance of security and efficiency. By creating a task that runs a stored procedure, converting data to CSV and using Java UDF to copy to an internal stage. A external process which is also monitoring the file and move to network directory using service account. It encapsulates logic within Snowflake and minimizes external dependencies. This approach avoids directly exposing Snowflake credentials to a third-party ETL tool or directly accessing the network drive from within Snowflake, which are security concerns. Option A involves writing directly to the network drive from within Snowflake, which may be complex to set up securely. Option B is generally not recommended due to external function overhead. Option C Introduces external dependency and Snowflake credentials needs to be managed carefully for the third party ETL tool. Option D is not possible since Snowpipe doesn't have capability to load into the network directory directly.

NEW QUESTION # 50
You are preparing to load a large dataset from Parquet files stored in an Azure Blob Storage container into Snowflake using Snowsight. The dataset contains personally identifiable information (PII) and you need to ensure that only authorized users can access this data,. You want to use Snowflake's data masking policies to protect the PII. Which of the following options represents the correct sequence of steps and considerations for achieving this, specifically using Snowsight for the loading and initial policy application phases?

A. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Define masking policies on the target table columns. 4. Create the target table with the desired schema, referring to masking policies. 5. Load the Parquet files directly into the target table using Snowsight.
B. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 3. Create a file format object specifying PARQUET as the file type. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.
C. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Define masking policies on the target table columns. 5. Load the Parquet files directly into the target table using Snowsight.
D. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Load the Parquet files into a staging table with all columns as VARCHAR using Snowsight. 4. Define masking policies on the staging table columns. 5. Create the final table and transfer data from staging, applying the masking policies on the target table as the data moves.
E. 1. Create an Azure external stage pointing to the Blob Storage container. 2. Create a file format object specifying PARQUET as the file type. 3. Create the target table with the desired schema, including data types. 4. Load the Parquet files directly into the target table using Snowsight. 5. Define masking policies on the target table columns after loading.

Answer: C

Explanation:
Option E is the most efficient and recommended approach. First, you need to set up the connection to Azure Blob Storage (the external stage) and define the file format for Parquet files. Then, you create the target table with the correct schema to receive the data. The critical step is to define the masking policies before loading the data. This ensures that the masking policies are in place when the data is loaded into the target table. Finally, load the data using Snowsight. Applying masking policies after loading the data (as in option B) is less efficient and leaves a window of opportunity where PII is exposed. Defining policies and then referring to them (as in option C) isn't the typical workflow. Option A is more complex and introduces a VARCHAR staging table, which isn't optimal for Parquet data.

NEW QUESTION # 51
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?

A. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.
B. Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.
C. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
D. Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
E. Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.

Answer: B,C,D,E

Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.

NEW QUESTION # 52
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

A. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.
B. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
C. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
D. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
E. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.

Answer: C,D

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

NEW QUESTION # 53
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