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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Loading and Unloading	- Data ingestion methods 1. Continuous ingestion and Snowpipe concepts 2. COPY INTO and bulk loading - Data export 1. UNLOAD and external stages
Topic 2: Security, Governance, and Data Sharing	- Data sharing and governance 1. Data masking and policies 2. Secure data sharing - Access control and security 1. Role-based access control (RBAC) 2. Authentication and encryption concepts
Topic 3: Data Modeling and Performance Optimization	- Performance tuning 1. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas
Topic 4: Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Cloud services layer, compute layer, storage layer 2. Virtual warehouses and scaling - Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake
Topic 5: Data Transformation and Analysis	- SQL-based transformations 1. Joins, aggregations, window functions 2. Semi-structured data (VARIANT, JSON, XML) - Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
Which action aids in performing a diagnostic analysis on historical data to identify reasons/causes of anomalies?

A. Focusing on isolated data points
B. Ignoring statistical trends in historical data
C. Collecting related data and demographics
D. Analyzing data solely from the past month

Answer: C

Explanation:
Collecting related data and demographics is crucial in understanding the reasons/causes of anomalies in historical data.

NEW QUESTION # 13
A marketing team wants to visualize website traffic data in Snowsight. They have a table named 'WEBSITE TRAFFIC' with columns 'VISIT DATE' (DATE), 'PAGE URL' (VARCHAR), and 'VISITOR COUNT' (INTEGER). The team wants to create a line chart showing the daily visitor count for a specific page, filtered by date range, allowing users to dynamically select the page URL from a dropdown. Which of the following steps are MOST effective and correct to achieve this using Snowsight dashboards?

A. Create a stored procedure in Snowflake that accepts 'start_date', 'end_date' , and as parameters, executes the query, and returns the result set. Call the stored procedure from the Snowsight dashboard, passing the filter values.
B. Write a Python script using the Snowflake Connector to query the data, process it using Pandas, and then generate a chart using Matplotlib. Embed the chart as an image in the Snowsight dashboard.
C. Create a view 'DAILY PAGE_VISITS using a SQL query like 'CREATE OR REPLACE VIEW DAILY PAGE_VISITS AS SELECT VISIT DATE, PAGE URL, AS TOTAL_VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT_DATE, PAGE_URL;'. Then, in Snowsight, create a dashboard and a line chart using this view with a filter on 'PAGE URL' and 'VISIT DATE', configured as dashboard variables.
D. Create a new chart in a Snowsight dashboard. Use a SQL query like 'SELECT VISIT_DATE, FROM WEBSITE_TRAFFIC WHERE PAGE URL = 'specific_page' AND VISIT DATE BETWEEN 'start_date' AND 'end_date' GROUP BY VISIT DATE ORDER BY VISIT DATE$. Manually update the query for each page.
E. Create a materialized view using 'CREATE MATERIALIZED VIEW AS SELECT VISIT_DATE, PAGE_URL, AS TOTAL VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT DATE, PAGE URL;'. In Snowsight, create a dashboard and a line chart using this materialized view with a filter on and configured as dashboard variables.

Answer: C,E

Explanation:
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.

NEW QUESTION # 14
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. 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.
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. 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.
E. 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.

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 # 15
You are creating a Snowsight dashboard to display the results of an A/B test on a website. You have the following tables: (columns: 'USER_ID', 'VARIANT' (VARCHAR, either 'A' or 'B'), 'CONVERSION' (BOOLEAN), 'TIMESTAMP') 'USER DEMOGRAPHICS' (columns: 'USER_ID, 'REGION', 'DEVICE) The stakeholders want to see the following visualizations: 1. Overall conversion rate for each variant. 2. Conversion rate for each variant broken down by region. 3. A table showing the statistical significance (p-value) of the difference in conversion rates between variants for each region, using a Chi-Square test. (Assume you have access to a stored procedure CHI SQUARE TEST(variant_a_conversions INT, variant_a_total INT, variant_b_conversions INT, variant b total INT) that returns the p-value.) Which combination of queries and Snowsight features will achieve the desired outcome with optimal performance and maintainability?

A. Create a stored procedure that takes 'start_date' and 'end_date' as parameters, performs all calculations (conversion rates and Chi-Square tests), and returns three result sets for the visualizations. Use the stored procedure as the data source for the Snowsight dashboard.
B. Create two views: 'VARIANT CONVERSION RATES': Calculates the overall and regional conversion rates. RESULTS': Executes for each region based on the view. Create three charts in Snowsight, using the views for the conversion rates and statistical significance table.
C. Create a task to aggregate the number of conversions and total views from the ab test data daily. Create views using the aggregated table to build the requested charts in Snowsight.
D. Create a single view 'COMBINED_DATX that joins and ' USER_DEMOGRAPHICS'. Use this view in Snowsight to create all three charts. Use calculated fields in Snowsight to determine conversion rates and call the stored procedure for p-value within the calculated field.
E. Create three separate charts using raw SQL queries for each visualizatiom Calculate the p-value outside of Snowflake using Python and load it into a new table. Use this new table to display a table chart with p-values.

Answer: B

Explanation:
Option B is the most efficient and maintainable. Creating two views allows for clean separation of concerns and reusability. 'VARIANT CONVERSION RATES pre-calculates the conversion rates, making the statistical significance calculation in cleaner and more performant. The resulting Snowsight dashboard is then simple to build using these views. Option A introduces unnecessary complexity by involving external tools (Python) and creating a new table. Option C attempts to do too much within Snowsight's calculated fields, which is not ideal for complex calculations like calling stored procedures. Option D might be performant, but makes the data presentation inflexible and ties the data to this specific dashboard. Option E is good to have the data aggregated, however it depends on option B to create the dashboard with the aggregated data.

NEW QUESTION # 16
You are designing a data warehouse for a retail company. The "SALES table stores transaction data and includes columns like 'TRANSACTION ID', 'PRODUCT ID', 'CUSTOMER ID, 'SALE DATE', and 'SALE AMOUNT'. The 'PRODUCT ID' references the 'PRODUCTS table, and 'CUSTOMER references the 'CUSTOMERS' table. Which of the following strategies represent the MOST optimal approach to define primary keys in this scenario, considering Snowflake's best practices and the need for efficient query performance, assuming 'TRANSACTION ID' is globally unique?

A. Define ' TRANSACTION_ID' as the primary key on the 'SALES' table. Define 'PRODUCT_ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID as the primary key on the 'CUSTOMERS table. Then, create unique indexes on 'PRODUCT ID in 'SALES referencing 'PRODUCTS and in referencing 'CUSTOMERS'.
B. Define 'TRANSACTION as the primary key on the 'SALES' table, 'PRODUCT ID as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID' as the primary key on the 'CUSTOMERS' table.
C. Define a composite primary key on the 'SALES' table consisting of 'TRANSACTION D', 'PRODUCT ID, and 'CUSTOMER ID. Define 'PRODUCT ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER_ID' as the primary key on the 'CUSTOMERS' table.
D. Define as the primary key on the 'SALES' table. Do not define primary keys on 'PRODUCTS' or 'CUSTOMERS'
E. Do not define primary keys on any of the tables. Rely solely on Snowflake's internal optimizations.

Answer: B

Explanation:
While Snowflake does not enforce primary key constraints, defining them provides valuable metadata for the query optimizer. Since TRANSACTION_ID' is unique in 'SALES', it is a suitable primary key. Defining primary keys on 'PRODUCTS' and 'CUSTOMERS' is also appropriate for their respective tables. Creating unique indexes is redundant if primary keys are defined. Skipping primary key definitions entirely can hinder optimization. A composite key in 'SALES' is unnecessary as 'TRANSACTION_ID is already globally unique.

NEW QUESTION # 17
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