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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
You are tasked with aggregating website clickstream data in Snowflake to identify the most popular product categories per region on a daily basis. The clickstream data is stored in a table named 'clickstream eventS with columns: 'event_time', 'user id', 'product id', 'region', and 'category'. You need to create a solution that efficiently identifies the top 3 categories for each region on each day. Which approach offers the best performance and scalability considering the dataset size is expected to grow significantly?

A. Using a combination of GROUP BY, RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3.
B. Using a combination of GROUP BY, DENSE_RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3. DENSE RANK handles ties more gracefully.
C. Using a simple GROUP BY operation to count the occurrences of each category, region, and day, then relying on external tools to filter the top 3.
D. Implementing a stored procedure that iterates through each region and day, calculating the category counts and selecting the top 3 using procedural logic.
E. Creating a series of temporary tables for each region, aggregating the data, and then using a JOIN operation to combine the results.

Answer: B

Explanation:
Option D is the most efficient and scalable because it leverages Snowflake's built-in window functions (DENSE_RANK) for efficient ranking within partitions (region and day). Window functions are optimized for parallel processing. DENSE RANK handles ties appropriately (assigning the same rank to tied categories). Options A is similar but DENSE_RANK is better for the case of ties. Option B would be slow and not scalable due to the iterative nature of stored procedures. Option C is inefficient due to the creation of temporary tables and JOIN operations. Option E offloads the crucial filtering to external tools, impacting performance.

NEW QUESTION # 12
You are tasked with loading data from a REST API that returns data in a deeply nested JSON format. The API requires authentication via an API key passed in the header. The JSON response contains an array of objects, each representing a customer order. You need to load only orders placed in the last 7 days. Which of the following steps and techniques would you use in Snowflake to achieve this?

A. Use Snowpipe with a continuous data loading pipeline, configure an external function to authenticate and load data into a temporary table then execute merge statement to apply it to the main table
B. Create an external function to call the REST API using API Integration object, filter the orders based on the timestamp within the UDF, and return the filtered data to Snowflake to be loaded into a table. Utilize secure secrets for API Key Storage.
C. Use a Python UDF to call the REST API, extract the relevant data, and load it into a Snowflake table.
D. Create a task with 'SYSTEM$STREAM' object to extract the data from API and apply it into Snowflake tables. Use 'SECRET object to store API Key.
E. Use Snowflake's 'COPY INTO' command with a preprocessor function to transform the JSON data and filter orders placed in the last 7 days. Store the API key in the preprocessor function.

Answer: B

Explanation:
Option B presents the most secure and robust approach. External Functions with API Integration objects allow calling external APIs. The filtering within the UDF ensures that only the required data is loaded, optimizing performance. Secure Secrets are essential for securely storing the API key. Option A is a viable solution but it requires careful security management of the API Key within Python code. Options C is incorrect since COPY INTO command doesn't support calling API with API Key in Header and it also require preprocessor function which are hard to implement. Option D is possible but doesn't mention API keys secure store which is less secure. Option E is not possible since 'SYSTEM$STREAM' object don't allow call API for data loading.

NEW QUESTION # 13
What does the "SQL keyword" refer to in the context of adding filters to a worksheet?

A. The table name containing the static filter "key" values
B. The name of the function used to generate the filter
C. The name of a User-Defined Function (UDF) used to define the filter "key" values
D. The filter name to be inserted into queries

Answer: D

Explanation:
In Snowsight (Snowflake's web interface), Data Analysts can create interactive dashboards and worksheets using Filters. When you define a filter (such as a Date Range or a list of Regions), you must assign it a SQL Keyword.
This "SQL Keyword" acts as a variable placeholder within your SQL code. For example, if you create a filter for "Customer Region" and set the SQL keyword to :my_region, you can then write a query like:
SELECT * FROM sales WHERE region = :my_region. When a user interacts with the UI and selects "North America" from the dropdown, Snowsight automatically injects "North America" into every instance where :
my_region appears in the worksheet's SQL.
Evaluating the Options:
* Option A is incorrect because the keyword is a label/variable, not the underlying function code.
* Option C and Option D are incorrect as they confuse the data source of the filter values with the reference name used in the SQL code.
* Option B is the correct answer. The SQL Keyword is specifically the identifier (prefixed with a colon in the code) that allows the analyst to link the UI element (the filter) to the execution logic of the query.
This is a fundamental skill for the Data Presentation and Data Visualization domain, ensuring reports are dynamic and user-friendly.

NEW QUESTION # 14
You are tasked with creating a dashboard to visualize website traffic data'. The data is stored in a Snowflake table 'WEB EVENTS' with the following columns: 'EVENT_ID', 'USER_ID', 'EVENT_TIMESTAMP', 'PAGE_URL', 'SESSION_ID', and 'DEVICE_TYPE. You need to calculate the average session duration for each device type, but the 'WEB EVENTS' table only contains individual events, not session start and end times. Assume a session is defined by a unique 'SESSION ID' for each 'USER ID. You are given a window function to calculate the start time and end time for a given 'SESSION ID': First_value and last_value. What steps are crucial to calculate average session duration by 'DEVICE TYPE?

A. Calculate the difference between the maximum and minimum 'EVENT _ TIMESTAMP' for each ' USER_ID. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.
B. Simply group by 'DEVICE_TYPE and calculate the average ' EVENT_TIMESTAMP'
C. Calculate median of event timestamp using percentile_cont. The calculate difference between median and max timestamps.
D. Calculate the sum of all 'EVENT TIMESTAMP' values for each 'USER ID. Group by 'DEVICE_TYPE and calculate the average.
E. Use window functions to determine the start and end 'EVENT TIMESTAMP for each 'SESSION ID. Calculate the difference between these start and end times. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.

Answer: E

Explanation:
Option B correctly identifies the steps to calculate session duration. First determine Session Start and End time. 1) Use window functions to identify the start and end timestamp for a given SESSION ID. 2) Calculate the difference between these timestamps. 3) Group by device type to arrive at the averages for session duration across the device_type. Option A is incorrect as it does not represent the session duration which needs to be calculated for a given Session_ID.

NEW QUESTION # 15
When maintaining reports and dashboards, why is it crucial to build automated and repeatable tasks?

A. Repeatable tasks hinder data updates in dashboards.
B. They ensure inconsistency in reports and dashboards.
C. Automated tasks reduce manual efforts, ensuring consistency.
D. Automated tasks increase the complexity of dashboard management.

Answer: C

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.

NEW QUESTION # 16
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