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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q200-Q205):

質問 # 200
A Snowflake data analyst needs to create a secure view called "masked customer data' based on an existing table named 'customer data'. The requirement is to mask the 'email' column for all users except those with the "DATA ADMIN' role. Also, only users with the 'ANALYST' role should be able to query any data from the view. The masking policy 'email_mask' has already been created. Which of the following sequence of commands correctly implements this requirement?

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

正解：A

解説：
Option C correctly implements the requirements. First, a 'SECURE VIEW' is created to ensure data security. Then, the 'ALTER VIEW' command correctly applies the masking policy to the column of the view. Finally, 'GRANT SELECT ensures that only the 'ANALYST role can query data from the view. Option B attempts to alter the underlying table directly which isn't the intention, and uses an invalid command 'MODIFY COLUMN'. Option A does not create a secure view. Option D uses incorrect syntax 'MODIFY COLUMN' for altering view. Option E try to alter the masked_customer_data, which is not a TABLE instead it is a View.

質問 # 201
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. Creating a series of temporary tables for each region, aggregating the data, and then using a JOIN operation to combine the results.
B. 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.
C. 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.
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. 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.

正解：C

解説：
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.

質問 # 202
Your company needs to comply with GDPR regulations, and you're tasked with implementing data masking to protect sensitive customer information. Specifically, you need to mask email addresses in a 'CUSTOMERS' table during data preparation. Which of the following approaches, utilizing Snowflake's system functions and data masking capabilities, offers the MOST secure and flexible solution, while allowing authorized users to potentially unmask the data if necessary?

A. Create a masking policy using the function to redact portions of the email address. Apply this policy to the EMAIL_ADDRESS column in the CUSTOMERS table.
B. Use the 'SHA2()' function with a salt to hash the email addresses. Authorized users can reverse the hash using the correct salt.
C. Create a masking policy that uses 'CASE statement based on IS_ROLE_IN_SESSION() function. When the role is Analyst role it should display original value, if not then it must display null value. Apply this policy to the EMAIL ADDRESS column in the CUSTOMERS table.
D. Create a view that uses 'REGEXP REPLACE()' function to redact portions of the email address. Grant access to the view instead of the table.
E. Use the 'MD5()' function to encrypt the email addresses. Authorized users can decrypt the MD5 hash using the appropriate key.

正解：C

解説：
Option C provides the best combination of security and flexibility. Snowflake's masking policies, combined with functions like , allow for dynamic masking based on user roles. Authorized users (e.g., those in an 'analyst' role) can see the original email address, while others see a masked version. Options A and D are not recommended as hashing/encryption functions like SHA2() and MD5() are not easily reversible and are not the intended use case for data masking policies. Option B offers masking with REGEXP_REPLACE but no mechanism to unmask with proper permissions. Option E creates a view, which is not centrally managed or scalable solution.

質問 # 203
You are developing a data pipeline that involves loading data from multiple CSV files stored in an Amazon S3 bucket into a Snowflake table. The files have different schemas (different column names and data types), but all files contain a common column named 'record_type' that identifies the schema of the data in that file. You need to create a single Snowflake table that can store data from all the files, while ensuring data integrity and proper data typing based on the 'record_type'. Which of the following approaches is the MOST efficient and scalable method to achieve this in Snowflake?

A. Use Snowflake's VARIANT data type for a single column in the Snowflake table. Load the entire CSV file content as a JSON string into this column. Create views to extract and cast the data based on the 'record_type'.
B. Create a single, wide Snowflake table with all possible columns from all CSV files, defining all columns as VARCHAR. Load all data into this table, then create views for each 'record_type' that cast the data to the correct data types.
C. Create a single external table pointing to the S3 bucket. Use a Snowflake stream on the external table to track changes. Implement a series of tasks, one for each 'record_type', that transform data from the stream and load into a single target table with appropriate datatypes.
D. Create multiple Snowflake tables, one for each 'record_type', with the corresponding schema. Use a Snowflake task that runs periodically to identify new files in the S3 bucket, determine their 'record_type', and load them into the appropriate table using a dynamic SQL query.
E. Create a single Snowflake table with a VARIANT column to store the raw data. Use a Snowpipe to load the data continuously. Implement a stored procedure that is triggered by the Snowpipe to parse the VARIANT data based on 'record_type' and insert it into correctly typed columns in the same table.

正解：D

解説：
The most efficient and scalable method involves creating multiple Snowflake tables, one for each 'record_type'. Loading data into correctly typed tables, using a task to dynamically identify files and load them into the appropriate table, leverages Snowflake's strengths. While VARIANT can store different schemas, querying and processing data in VARIANT columns are less performant than querying and processing data with correct data types. A single wide table with all VARCHAR columns requires extensive casting in views. External tables with streams can work, but creating multiple tasks for each record type becomes complex. Choosing the correct data type at load time and having well-defined schemas will minimize transformation later.

質問 # 204
You observe that a Snowflake query, intended to perform aggregations on a 'SALES table (partitioned by 'SALE DATE), exhibits unexpectedly poor performance despite the data being relatively well clustered. Further investigation reveals that a user recently modified the 'SESSION' parameter NTE OUTPUT FORMAT to 'YYYY-MM'. The aggregation query filters the 'SALES' table using a 'WHERE clause on 'SALE DATE. Which of the following explains the performance degradation, and what actions can be taken to remediate?

A. The change in impacts the cost-based optimizer and impacts the explain plan, causing a full table scan, use 'ALTER SESSION SET DATE OUTPUT FORMAT = 'AUTO".
B. The parameter is irrelevant to query performance as it only affects the output representation of dates. The performance issue is due to a different factor, such as insufficient warehouse size.
C. The change in increases the size of the query's result set, leading to network bottlenecks. Reduce the number of columns returned by the query.
D. The modified causes Snowflake to perform implicit conversions on 'SALE_DATE in the 'WHERE clause, preventing partition pruning. Modify the query to use a consistent date format or reset the session parameter.
E. The change in alters the internal storage format of 'SALE_DATE, invalidating existing clustering metadata. Re-clustering the 'SALES' table is required.

正解：A、D

解説：
The parameter itself doesn't change underlying data or invalidate clustering directly (A). While a larger result set can impact network (C), it's less likely than partition pruning issues in this scenario. 'DATE OUTPUT FORMAT can affect query performance if it causes implicit conversion on 'DATE columns in 'WHERE clauses, which can prevent partition pruning; setting it back to 'AUTO' or default behavior fixes this. The optimizer can be affected, forcing full table scan which is sub-optimal.

質問 # 205
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