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Snowflake DAA-C01 Pass-Sure Valid Test Papers

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q256-Q261):

NEW QUESTION # 256
A data analyst is tasked with optimizing query performance for a frequently accessed report that calculates daily sales totals for different product categories. The report currently takes an unacceptable amount of time to generate. The underlying table 'SALES DATA contains columns: 'ORDER DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), (NUMBER). Which of the following database objects would be MOST effective to significantly improve the report's performance without requiring changes to the existing SQL query used for the report?

A. A temporary table created before running the report.
B. A materialized view created using the same SQL query as the report.
C. An external table pointing to the same data source as 'SALES_DATR.
D. A standard view created using the same SQL query as the report.
E. A sequence object to pre-calculate sales amounts.

Answer: B

Explanation:
A materialized view stores the results of the query, so subsequent queries can retrieve the pre-computed results directly. This avoids the need to re-run the query each time the report is generated, significantly improving performance. A standard view is just a stored query and doesn't store data. An external table is used for data residing outside of Snowflake. A temporary table would only be useful during the session it's created in and would need to be populated, adding to processing time. A sequence object is used for generating unique numbers, not for calculating sales amounts.

NEW QUESTION # 257
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?

A. Create a 'SALES LAST 30 DAYS' table containing only the last 30 days of sales, and join that table to the 'CUSTOMERS table. Refresh the 'SALES LAST 30 DAYS daily.
B. Create a standard index on 'SALES.CUSTOMER ID and 'CUSTOMERS.CUSTOMER ID.
C. Create a materialized view joining the two tables and filter on 'SALE_DATE. Refresh the materialized view daily.
D. Create a search optimization on 'SALES.CUSTOMER and 'CUSTOMERS.CUSTOMER ID.
E. Create a cluster key on 'SALES' table on 'CUSTOMER_ID' and re-cluster the table after data loading.

Answer: A

Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.

NEW QUESTION # 258
You are loading data into a Snowflake table 'PRODUCT PRICES' using the COPY INTO command from a Parquet file stored in Azure Blob Storage. The 'PRODUCT PRICES' table has the following schema: VARCHAR, PRICE CURRENCY VARCHAR, LAST UPDATED TIMESTAMP NTZ. The Parquet file contains all these columns, but the 'LAST UPDATED column is stored as a Unix epoch timestamp (seconds since 1970-01-01 00:00:00 UTC). You need to transform the Unix epoch timestamp to a Snowflake TIMESTAMP NTZ during the data load. Which of the following options correctly demonstrates how to achieve this using a transformation within the COPY INTO command?

Answer: D

Explanation:
The correct answer is C. When using transformations with COPY INTO and an external stage, you must explicitly select the columns using '$1', '$2, etc. The gazure_stage/data.parquet' path is used in the 'FROM' clause, and converts the fourth column (LAST_UPDATED) from the Parquet file to a Snowflake TIMESTAMP. Note: It assumes column ordering in the Parquet file matches the order in the select statement. Using a SELECT statement within the FROM' clause is the correct approach when applying transformations directly during the COPY. The other options are incorrect because they either use the deprecated 'TRANSFORMATION' parameter (now replaced by select statements in the FROM clause) or attempt to apply transformations incorrectly. This is the only answer that is correct and will work. Options A, B, D, and E will all fail during the COPY INTO statement.

NEW QUESTION # 259
You're building a dashboard to monitor the performance of various marketing campaigns. The data resides in Snowflake, and you're using a BI tool that supports direct query The table has columns: 'CAMPAIGN ID, 'DATE, 'IMPRESSIONS', 'CLICKS, 'SPEND , and You need to create a calculated field in the BI tool representing the Cost Per Conversion (CPC), but you want to optimize query performance and avoid division by zero errors. Assume 'SPEND' and 'CONVERSIONS' are both numeric columns. Which SQL expression, suitable for use in a direct query BI tool, is the MOST performant and robust way to calculate CPC, avoiding zero conversion issues?

Answer: C

Explanation:
The ' DIV0' function is specifically designed by Snowflake to handle division by zero gracefully, returning NULL. It's the most concise and performant way to achieve the desired result. 'NULLIF(CONVERSIONS, 0)' is also correct way, but DIVO is more accurate for Snowflake environment. 'CASE WHEN' and 'IFF are functionally equivalent in this scenario, but is shorter to write. ZEROIFNULL' will return 0 when input is null which won't solve the zero conversion issues. Furthermore ZEROIFNULL' is not a valid Snowflake function.

NEW QUESTION # 260
You are tasked with loading data from an S3 bucket into a Snowflake table named 'SALES DATA'. The data is in CSV format, compressed with gzip, and contains a header row The S3 bucket requires AWS IAM role authentication. The 'SALES DATA' table already exists, and you want to use a named stage for this ingestion process. Which of the following steps are necessary to successfully load the data, minimizing administrative overhead?

A. Create a Snowflake stage object that references the storage integration, the S3 bucket URL, and specifies the file format (CSV with gzip compression and header skip). Use the 'COPY INTO' command referencing the stage.
B. Ensure the S3 bucket has public read access; Snowflake's COPY INTO command will handle decompression and data loading without further configuration.
C. Create a new IAM role in AWS with access to the S3 bucket, then create a Snowflake storage integration object referencing that role's ARN and the S3 bucket's URL.
D. Create an external function to read the data from S3 and then insert it into the table, as Snowflake cannot directly read gzipped CSV files from S3.
E. Grant usage privilege on the storage integration to the role performing the data load. Ensure the user loading data has access to the Snowflake stage and the ' INSERT privilege on the table.

Answer: A,C,E

Explanation:
Options A, B, and D are correct. Option A: Snowflake needs an IAM role and storage integration to access the S3 bucket securely. Option B: A stage object simplifies the COPY INTO command and handles file format details. Option D: Correct permissions are required for the data load to succeed. Option C is incorrect because Snowflake can directly read gzipped CSV files from S3 when configured correctly. Option E is incorrect because granting public read access to the S3 bucket is a security risk and is not the best practice; using IAM roles provides controlled and secure access.

NEW QUESTION # 261
......

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