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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. Define primary keys for tables 2. Implement constraints 3. Perform table joins between parent/child tables - 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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 4. Identify elements required for business goals using BI reports or SQL analysis 5. Determine the level of data granularity required - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from structured sources (CSV) 3. Retrieve data from unstructured sources 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Create tables and views 3. Find external data sets that correlate with available data - Implement data processing solutions 1. Use logging and monitoring solutions (auditing, data lineage) 2. Cleanse, conform, and enrich data 3. Automate and implement data pipelines (scheduling) 4. Respond to processing failures
Topic 2: Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Present analytical results 2. Snowsight dashboards - Integrate with BI tools 1. Other partner visualization tools 2. Power BI integration 3. Tableau integration
Topic 3: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions
Topic 4: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. PIVOT/UNPIVOT operations 2. QUALIFY clauses 3. Window functions 4. Common Table Expressions (CTEs) - Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
What is the primary benefit of connecting BI tools to Snowflake for dashboard creation?

A. Seamless integration and data visualization
B. Simplified data access for all users
C. Improved data security in dashboards
D. BI tools restrict dashboard customization

Answer: A

Explanation:
Connecting BI tools to Snowflake enables seamless integration and data visualization in dashboard creation.

NEW QUESTION # 66
A data analyst needs to enrich customer data in a Snowflake database with demographic information obtained from the Snowflake Marketplace. The purchased listing provides data as a secure view Which of the following SQL commands is the MOST efficient and secure way to create a new table in the data analyst's database that combines customer data with the demographic information from the Marketplace listing, while ensuring that only necessary columns from the Marketplace data are included?

Answer: B

Explanation:
Option D is the most efficient and secure because: it creates a new table or replaces if one exists, it explicitly selects only the necessary columns (age, income) from the Marketplace view, preventing unnecessary data exposure. It also explicitly uses JOIN condition. A new table has to created instead of view for persistence and to prevent recomputing of the data on frequent requests.

NEW QUESTION # 67
How does leveraging partition pruning optimize query performance in Snowflake?

A. Increases storage requirements for optimized query access
B. Limits query complexity and optimization possibilities
C. Reduces data accessibility across multiple warehouses
D. Filters unnecessary partitions during query execution

Answer: D

Explanation:
Partition pruning in Snowflake filters unnecessary partitions during query execution, enhancing query performance by minimizing the amount of data scanned.

NEW QUESTION # 68
You have a Snowflake table named 'sensor_data' with a column 'reading' containing JSON data'. The JSON structure varies, but you want to extract a specific nested value, 'temperature', using a UDE The path to 'temperature' might be different depending on the 'sensor_type'. Some sensors have the temperature at '$.metrics.temperature' , others at '$.reading.temp_c'. The sensor type is stored in the 'sensor_type' column. You want to create a UDF named which takes the JSON 'reading' and the 'sensor_type' as input and extracts the temperature, returning NULL if the path does not exist in the JSON. How can you implement this using a JavaScript UDF and Snowflake's JSON parsing functions for optimal performance?

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

Answer: B

Explanation:
Option C is the MOST optimal. It uses 'VARIANT as the input data type for 'reading' , which avoids the unnecessary parsing of JSON using as Snowflake automatically parses JSON data into a VARIANT type. It also includes a 'try...catch' block to handle cases where the specified path does not exist within the JSON, returning 'NULL' as required. This prevents errors from halting the query. Using 'VARIANT directly and exception handling offers superior performance. Option A and D parse VARIANT as String, leading to parsing overhead. B misses the try catch block and is prone to failure when temp is not available for a given sensor. Option E is less efficient than option C due to using array notation.

NEW QUESTION # 69
You have a large table 'WEB EVENTS with columns 'EVENT TIMESTAMP, 'USER ID', 'PAGE URL', and 'EVENT _ TYPE. You need to create a materialized view that efficiently calculates the daily unique user count for a specific set of 'PAGE URL' values. The 'WEB EVENTS table is frequently updated. Which of the following approaches would be MOST performant and scalable for this scenario?

A. Create a materialized view that first calculates the total number of events for each user on each day and then aggregates that data to calculate the unique user count.
B. Ingest the daily unique user count data via a 3rd party tool into a new table and create a view using that table.
C. Create a materialized view that directly selects 'COUNT(DISTINCT USER_ID)' grouped by ' and with filtering on the desired 'PAGE URL' values.
D. Create a standard view that filters the 'WEB_EVENTS' table and calculates 'COUNT(DISTINCT USER_ID)' grouped by and 'PAGE URL'.
E. Create a materialized view using a window function to calculate the running total of unique users each day, then extract the final value for each day.

Answer: C

Explanation:
Calculating 'COUNT(DISTINCT directly in the materialized view is the most efficient approach. Pre-calculating unnecessary aggregates adds overhead. Standard views do not provide the performance benefits of materialized views. Window functions are generally less performant than direct aggregations in this scenario, and the new data can be calculated directly in Snowflake, avoiding dependency with 3rd party tools.

NEW QUESTION # 70
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