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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?

A. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
B. SELECT TRIM(REGEXP >', FROM COMMENTS;
C. SELECT >', FROM COMMENTS WHERE
D. SELECT >', FROM COMMENTS;
E. SELECT >', comment_text) FROM COMMENTS;

Answer: D

Explanation:
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.

NEW QUESTION # 93
A data analyst is working with a large table partitioned by (DATE type). The table contains millions of rows spanning several years. They need to optimize a query that retrieves sales data for a specific quarter of 2023. The initial query is: 'SELECT FROM sales_data WHERE EXTRACT(YEAR FROM sale_date) = 2023 AND EXTRACT(QUARTER FROM sale_date) = To improve performance using partition pruning, which of the following queries is the MOST efficient alternative?

Answer: B

Explanation:
Option A is the most efficient because it directly uses the 'sale_date' column with a 'BETWEEN' clause using specific date values. This allows Snowflake to directly leverage the partition pruning based on the date range. Options B and C use functions C YEAR, 'QUARTER on the 'sale_date' column, preventing efficient partition pruning. Option D uses 'LIKE', which is not suitable for date comparisons and would likely result in a full table scan, furthermore 'LIKE operator will not work with Date Data type. Option E does not prune to a specific quarter.

NEW QUESTION # 94
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?

A. Disable Snowpipe and switch to a batch loading approach using the COPY INTO command with automatic data compression. The COPY INTO command handles larger files more efficiently than Snowpipe.
B. Configure the S3 bucket to automatically split oversized JSON files into smaller files before they are sent to the SQS queue. Snowpipe will then process these smaller files independently.
C. Use the 'VALIDATE function in Snowflake to identify oversized JSON records. Then, manually extract and split those records into smaller files and load them separately.
D. Increase the 'MAX FILE SIZE parameter of the Snowpipe configuration to accommodate the larger JSON records. Snowflake automatically handles oversized records by splitting them internally.
E. Implement a pre-processing step using an AWS Lambda function triggered by S3 events to split the oversized JSON records into smaller, valid-sized chunks before they are ingested by Snowpipe. Update the Snowpipe COPY statement to handle the new chunked data format.

Answer: E

Explanation:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.

NEW QUESTION # 95
What are important factors to consider when creating tables and views in Snowflake? (Choose threE.

A. Optimization for query performance
B. Documentation and metadata management
C. Data security and access controls
D. The color of the tables and views

Answer: A,B,C

NEW QUESTION # 96
You are designing a data warehouse for a retail company. The company needs to analyze sales data based on product category, customer demographics, and store location. The sales data is initially stored in a semi-structured JSON format with nested arrays for product details and customer information. The BI team requires optimized query performance for aggregations across these dimensions. Which approach is most suitable for this scenario?

A. Load the JSON data directly into a VARIANT column and use lateral views for querying. Avoid any data modeling to minimize initial effort.
B. Create a single, wide denormalized table containing all sales, product, customer, and store information.
C. Create a flattened relational data model with separate tables for sales, products, customers, and store locations, linked using foreign keys.
D. Load the JSON data directly into Snowflake and rely solely on Snowflake's query optimization capabilities without any data modeling.
E. Use a hybrid approach: flatten only the customer demographics into a relational table and keep the product details in a VARIANT column for ad-hoc queries.

Answer: C

Explanation:
Option B is the most suitable approach. A flattened relational data model with separate tables and foreign keys allows for efficient querying and aggregations across different dimensions, which is a key requirement for BI reporting. Flattening the data reduces the overhead of parsing JSON during query execution and enables the use of standard SQL aggregation functions. Option A can lead to performance issues with complex JSON structures. Option D can lead to data redundancy and update anomalies. Option C offers a hybrid approach but can still be inefficient for certain queries. Option E relies too heavily on Snowflake's automatic optimization and will likely underperform compared to a properly designed data model.

NEW QUESTION # 97
......

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Snowflake DAA-C01 Valid Exam Experience, Latest DAA-C01 Dumps Ppt

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q92-Q97):

The Best Snowflake DAA-C01 Exam Questions

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