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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q125-Q130):

NEW QUESTION # 125
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 >', FROM COMMENTS WHERE
B. SELECT >', FROM COMMENTS;
C. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
D. SELECT TRIM(REGEXP >', FROM COMMENTS;
E. SELECT >', comment_text) FROM COMMENTS;

Answer: B

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 # 126
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?

A. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.
B. Create a materialized view and set the refresh schedule to 'ON CHANGE'.
C. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on
D. Create a materialized view and schedule a daily refresh at a time of low system activity.
E. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.

Answer: D

Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.

NEW QUESTION # 127
When maintaining reports and dashboards, why is it crucial to configure subscriptions and updates?

A. Configuring these features hampers dashboard usability.
B. They limit data accessibility for effective dashboard usage.
C. They complicate dashboard management without any added benefits.
D. Subscriptions and updates ensure timely information delivery.

Answer: D

Explanation:
Subscriptions and updates ensure timely information delivery, a crucial aspect of maintaining reports and dashboards.

NEW QUESTION # 128
You are designing a data pipeline to ingest JSON data from an external stage (AWS S3) into a Snowflake table called 'ORDERS' Some of the JSON files contain nested arrays that need to be flattened and transformed during the loading process. You have already defined a VARIANT column in the 'ORDERS table to store the raw JSON data'. However, occasionally, some files fail to load completely, and the 'SYSTEM$PIPE STATUS' shows a 'LOAD FAILED' status without providing granular details about the specific records causing the failure. Which of the following strategies, used IN COMBINATION, would be MOST effective in troubleshooting and resolving these failures while minimizing the impact on the overall data ingestion process?

A. Implement a pre-processing step using a Snowflake task to validate the JSON data before loading it into the 'ORDERS' table using a COPY INTO statement. The Task would filter out bad records.
B. Enable Snowpipe's 'ERROR_INTEGRATION' , examine the error logs for malformed JSON records, and adjust the COPY INTO statement with appropriate FILE_FORMAT options to handle the nested arrays.
C. Use a 'VALIDATE' statement with the same COPY INTO statement to identify records that will fail. Then, modify the COPY INTO statement to handle the errors or exclude the problematic records using a 'WHERE clause in the transformation logic of the COPY INTO statement or inline SQL Transformations
D. Enabling the ERROR=CONTINUE parameter on the COPY INTO statement used in the pipe, and regularly querying the function to identify issues in the loaded data.
E. Increase the compute resources allocated to the virtual warehouse used by the Snowpipe. Also, disable Snowpipe and load the data manually using the COPY INTO command to identify any errors during load.

Answer: B,C

Explanation:
ERROR INTEGRATION' allows you to inspect individual error records and identify patterns in those failing files. The 'VALIDATE' function allows you to perform a COPY INTO using similar parameters as your copy into statement to validate the record, and helps you tune your data pipeline for errors. Option B is viable, but has increased maintenance overhead compared to VALIDATE, because you would need to write code for the preprocessing. Option D focuses on resource allocation, which doesn't directly address data quality issues. Option E by itself only attempts to continue, and doesn't do any validation. 'ON is a good idea when paired with validating the data after the load.

NEW QUESTION # 129
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: 'PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) 'SALES' (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) 'INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

A. Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.
B. Create a single, wide denormalized table containing all the necessary data from the 'PRODUCTS, 'SALES, and 'INVENTORY tables using JOINs. Build the dashboard directly on this table.
C. Utilize Snowflake's search optimization service on relevant columns (e.g., PRODUCT ID, REGION) in the base tables and use standard JOINs and aggregations within views used by the dashboard.
D. Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.
E. Create materialized views that pre-aggregate sales data by region, product category, and time period (e.g., daily, weekly, monthly). Join these materialized views with product and inventory data in the dashboard queries.

Answer: C,E

Explanation:
Search optimization (C) can significantly speed up queries on large tables by creating a search index on frequently used filter columns. Materialized views (D) are also beneficial because they pre-aggregate the data, reducing the amount of computation required at query time. Creating a single, wide denormalized table (A) can lead to data redundancy and increased storage costs. Joining separate views in the dashboard tool (B) can be inefficient, as the joins are performed at query time. Data masking policies (E) are important for security but don't directly optimize query performance for dashboards.

NEW QUESTION # 130
......

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