DAA-C01考試指南 & DAA-C01試題

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Snowflake DAA-C01 Exam Syllabus Topics:

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

>> DAA-C01考試指南 <<

Snowflake DAA-C01認證考試學習指南

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最新的 SnowPro Advanced DAA-C01 免費考試真題 (Q35-Q40):

問題 #35
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?

答案：B

解題說明：
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.

問題 #36
You are tasked with analyzing website clickstream data stored in a Snowflake table called 'clickstream_events'. Each row represents a click event and contains a 'session_id' , and a 'properties' column of type VARIANT that stores key-value pairs related to the event (e.g., '(page': '[product/123', 'element': You need to extract the 'page' and 'element' values from the 'properties' column and identify the most common 'page'-'element' combinations for each 'session_id'. Furthermore you need to limit the results of your data to the top 5 pages element pair. How can this task be accomplished using Snowflake table functions and analytical functions?

A. First create a view that flattens the JSON column using LATERAL FLATTEN, then select from this view to perform the group by and ranking operations.
B. Use multiple LATERAL FLATTEN calls, one for 'page' and one for 'element', then JOIN the results on and use QUALIFY ROW_NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
C. Extract 'page' and 'element' using 'properties:page' and 'properties:element' directly in the SELECT statement, then use GROUP BY 'session_id', 'page', 'element' and QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
D. Use LATERAL FLATTEN to extract the keys and values from the 'properties' column, then use GROUP BY 'session_id', 'key', 'value' and COUNT( ) to find the most frequent combinations.
E. Create a UDF to parse the 'properties' VARIANT and return a table with 'page' and 'element columns, then JOIN this UDF's output with the original table and use QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.

答案：C

解題說明：
Option C is the most efficient and Snowflake-idiomatic way to achieve this. Directly accessing 'properties:page' and properties:element' is more performant than using LATERAL FLATTEN when you know the specific keys you need. The QUALIFY clause, combined with ROW NUMBER(), efficiently filters the results to the top 5 combinations per session. LATERAL FLATTEN is generally used when you need to iterate over an array within the VARIANT, not when you're extracting specific key-value pairs. UDF introduces extra overhead.

問題 #37
You've identified a 'Filter' operation in a Snowflake query execution plan that is consuming a significant amount of time. The filter predicate involves a UDF (User-Defined Function) called 'calculate_score(columnl, column2)'. The UDF is written in Python. Analyzing the plan, you observe a high number of rows being processed by this filter. How can you optimize this scenario for faster query execution?

A. Increase the warehouse size to provide more resources for UDF execution.
B. Replace the UDF with a regular expression to mimic the calculation to increase performance
C. Rewrite the UDF in SQL instead of Python to leverage Snowflake's native execution engine.
D. Implement caching within the UDF to store previously calculated scores and reuse them for identical inputs.
E. Create a materialized view that pre-calculates the score using the 'calculate_score' UDF and stores the results. The query should then filter on the materialized view.

答案：C,E

解題說明：
Options A and C provide significant performance improvements. A moves the computation to Snowflake's engine, likely improving speed. C pre-calculates the score, avoiding repeated UDF calls. While increasing warehouse size (B) might help, it doesn't address the fundamental inefficiency of the UDF. Caching within the UDF (E) can improve performance if there are repeated calls with the same inputs, but a SQL based code or materialized view is better solution in this case. Regular Expressions don't have computational power for any complex calculations.

問題 #38
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?

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

答案：A

解題說明：
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().

問題 #39
You have Parquet files containing customer order data in an external stage. You need to create a secure view in Snowflake that filters the data based on user roles. Only users with the 'SALES REP' role should see orders from their assigned region, while 'MANAGER' roles should see all data'. Assuming you have set up appropriate role hierarchies, how would you implement this using row-level security policies with Parquet external tables?

A. Create an external table with a schema defined according to the Parquet files. Define a masking policy that hides the sensitive information for users without proper roles. Create a standard view on top of the external table. Apply the masking policy to a column inside view.
B. Create a standard view querying the external table. Create a row access policy that filters the data based on the user role and the region. Apply the policy to the external table. Delegate the ownership to 'ACCOUNTADMIN'.
C. Create a secure view that directly queries the Parquet files using to access the region information. Apply a WHERE clause in the view definition using to filter based on the user's role. No RLS policy needed.
D. Create a secure view that queries the Parquet files directly using 'JSON EXTRACT PATH TEXT to access the region information. Apply a JOIN operation with a role table to filter the data based on the user's role.
E. Create a secure view that queries the external table. Then, define a row access policy that filters the data based on the user role and the region, and apply the policy to the secure view. The policy should use to determine the appropriate filtering criteria. Delegate the ownership to 'ACCOUNTADMIN'.

答案：E

解題說明：
Row Access Policies (RAP) are designed for Row Level Security, and they are applied to tables or views. Since the data is in an external table accessed through a view, the policy should be attached to the secure view. The ' function is used to determine the user's current role within the policy's logic. Secure Views protect the underlying data and policy logic from unauthorized access. Delegating ownership to 'ACCOUNTADMIW is not strictly required but a good security practice. Masking policies aren't the correct choice here as it is used to hide data from the user.

問題 #40
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