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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
When using Snowflake functions, how do scalar functions differ from aggregate functions?

A. Aggregate functions return single values.
B. Aggregate functions modify table structures.
C. Scalar functions work on groups of rows.
D. Scalar functions operate on entire tables.

Answer: A

Explanation:
Aggregate functions return single values from groups of rows, distinguishing them from scalar functions.

NEW QUESTION # 42
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

A. Leveraging Snowflake's dynamic data masking and data classification capabilities to maintain data security and compliance while adhering to real-time data ingestion.
B. Using a scheduled task to query the API every minute and store the data directly into a Snowflake table with a materialized view calculating the rolling average.
C. Using a traditional ETL tool to extract, transform (calculate rolling average), and load the data into Snowflake in 15-minute intervals.
D. Storing the raw data into Snowflake using Snowpipe in micro-batches and creating a VIEW that performs the rolling average calculation on-demand.
E. Employing a stream processing framework (e.g., Apache Kafka) to ingest the data, perform the rolling average calculation using a tumbling window, and load the aggregated results into Snowflake.

Answer: E

Explanation:
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.

NEW QUESTION # 43
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: 'PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) 'SALES' (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) 'INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

A. Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.
B. Create materialized views that pre-aggregate sales data by region, product category, and time period (e.g., daily, weekly, monthly). Join these materialized views with product and inventory data in the dashboard queries.
C. Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.
D. Utilize Snowflake's search optimization service on relevant columns (e.g., PRODUCT ID, REGION) in the base tables and use standard JOINs and aggregations within views used by the dashboard.
E. Create a single, wide denormalized table containing all the necessary data from the 'PRODUCTS, 'SALES, and 'INVENTORY tables using JOINs. Build the dashboard directly on this table.

Answer: B,D

Explanation:
Search optimization (C) can significantly speed up queries on large tables by creating a search index on frequently used filter columns. Materialized views (D) are also beneficial because they pre-aggregate the data, reducing the amount of computation required at query time. Creating a single, wide denormalized table (A) can lead to data redundancy and increased storage costs. Joining separate views in the dashboard tool (B) can be inefficient, as the joins are performed at query time. Data masking policies (E) are important for security but don't directly optimize query performance for dashboards.

NEW QUESTION # 44
You have a Snowflake table 'RAW DATA containing a 'VARIANT column called 'json_data'. This column stores JSON objects representing customer orders. The structure includes a nested array of items within each order. You need to create a flattened table called 'ORDER ITEMS with the following columns: 'order_id', and However, the field is not directly present in the JSON data'. Instead, it needs to be derived by concatenating the 'order_id' with the index (ordinal position) of the item within the 'items' array. The structure looks like this: { "order_id": "ORD-123", "customer_id": "CUST-456", "items": [ { "item_name": "Laptop", "item_price": 1200 }, { "item_name": "Mouse", "item_price": 25 } ] } Which of the following SQL statements correctly creates the 'ORDER ITEMS table?

A. Option E
B. Option A
C. Option B
D. Option D
E. Option C

Answer: A

Explanation:
Option E correctly uses the 'LATERAL FLATTEN' function to unnest the 'items' array. The key is using 'f.seq' (sequence number) provided by 'FLATTEN' function, which is the ordinal position of the item in the array (starting from 1), to create the item_id. It concatenates the order_id with the sequence number to generate a unique item_id. Option A is wrong because row_number is an aggregate function, and needs Group by to execute. Option B uses f.index, which does not exist in the output of Lateral flatten. Options C is correct in most of the parameters, however, 'raw_data' alias is missing, as a result the result will be error. Option D also uses seq, however it adds 1, which changes the index to start from 1, which might be wrong.

NEW QUESTION # 45
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')

A. Option E
B. Option A
C. Option B
D. Option D
E. Option C

Answer: A

Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.

NEW QUESTION # 46
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