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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
In what ways do stored procedures differ from user-defined functions (UDFs) in SQL?

A. Stored procedures and UDFs are interchangeable in SQL.
B. Stored procedures can't execute repetitive tasks like UDFs.
C. UDFs allow custom-defined operations on data, extending SQL functionalities.
D. Stored procedures only handle basic arithmetic operations.

Answer: B

Explanation:
Stored procedures and UDFs differ in their ability to execute repetitive tasks.

NEW QUESTION # 63
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 E
B. Option C
C. Option A
D. Option B
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 # 64
You are working with a Snowflake environment where data analysts need to query Parquet files located in AWS S3. The analysts require the ability to perform complex aggregations and joins with other Snowflake tables. Some of the Parquet files have evolved their schema over time, adding new columns. Given these requirements, which set of actions would provide the MOST efficient and maintainable solution? Choose the THREE most appropriate actions.

A. Create an external table with 'AUTO REFRESH = TRUE' and use schema evolution features to automatically detect and accommodate new columns in the Parquet files. use parameter to tune query performance.
B. Implement a process to periodically load the Parquet files into Snowflake internal tables using 'COPY INTO , inferring the schema from the Parquet files and creatin a new table version whenever the schema changes. Create views to handle the multi le table versions.
C. Create an external table in Snowflake pointing to the S3 bucket and use the 'VALIDATE(100);' command frequently to validate if the stage data integrity
D. Implement a data pipeline that continuously ingests the Parquet data into a Snowflake internal table with a defined schema, ensuring all possible columns are included and using a VARIANT type for potentially evolving fields.
E. Create materialized views on top of the external table, extracting frequently used columns and performing common aggregations to pre-compute results.
F. Develop custom SQL scripts to manually extract data from the external table into temporary tables with specific schemas, then perform aggregations and joins using those temporary tables.

Answer: A,C,E

Explanation:
'AUTO REFRESH = TRUE' handles schema evolution in external tables. Materialized views on the top of external tables provides precomputed data, which is good for aggregations. The VALIDATE command allows analysts to check the stage data integrity and avoid errors in the data pipeline. Ingesting all data into a single VARIANT column is not efficient for aggregations and joins. Extracting to temporary tables is not maintainable. Periodically creating new tables is complex to manage. Adjusting can help improve performance.

NEW QUESTION # 65
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

A. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
B. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
C. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.
D. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
E. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.

Answer: A,B

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.

NEW QUESTION # 66
When employing different data models (e.g., dimensional, Data Vault) in Snowflake, how does Data Vault modeling address complexities in data integration and changes?

A. Data Vault models simplify data integration and adapt well to changes
B. Data Vault models enhance data transformation capabilities
C. Data Vault models offer limited scalability and flexibility
D. Data Vault models restrict data access to specific user roles

Answer: A

Explanation:
Data Vault models simplify data integration and adapt well to changes, providing advantages in handling complexities and evolutions in data structures.

NEW QUESTION # 67
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