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Free PDF Quiz 2026 Snowflake DAA-C01: First-grade SnowPro Advanced: Data Analyst Certification Exam Latest Study NotesPDFVCE aims to assist its clients in making them capable of passing the Snowflake DAA-C01 certification exam with flying colors. It fulfills its mission by giving them an entirely free SnowPro Advanced: Data Analyst Certification Exam (DAA-C01) demo of the dumps. Thus, this demonstration will enable them to scrutinize the quality of the Snowflake DAA-C01 study material. Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q68-Q73):NEW QUESTION # 68
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. Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.
B. 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.
C. 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.
D. 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.
E. Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.
Answer: B,D
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 # 69
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(REGEXP >', FROM COMMENTS;
B. SELECT >', FROM COMMENTS;
C. SELECT >', comment_text) FROM COMMENTS;
D. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
E. SELECT >', FROM COMMENTS WHERE
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 # 70
You are designing a data pipeline in Snowflake that ingests data from multiple external sources with varying schemas and data quality. After ingestion, you need to standardize the data format, handle missing values, and perform data type conversions before loading it into your analytical tables. You need to implement a reusable and maintainable solution. Which approach minimizes code duplication and maximizes data quality?
A. Use Snowflake's pipes and Snowpipe to load raw data into staging tables, then use a combination of dynamic SQL, user-defined functions (UDFs), and stored procedures to perform the data cleaning and transformation in a modular and reusable manner.
B. Implement a centralized stored procedure that accepts the data source name as a parameter and performs all data cleaning and transformation logic based on conditional statements (CASE statements).
C. Create separate SQL scripts for each data source to handle the specific data cleaning and transformation requirements.
D. Use Snowflake's external tables to directly query the data in its raw format and perform the data cleaning and transformation on-the-fly during query execution.
E. Ingest the data into a single large table without any transformation, and rely on business intelligence tools to handle data cleaning and transformation during analysis.
Answer: A
Explanation:
Option C is the most robust and maintainable approach. It leverages Snowflake's features (pipes, Snowpipe, dynamic SQL, UDFs, and stored procedures) to create a modular and reusable data pipeline. Pipes and Snowpipe handle data ingestion efficiently. Dynamic SQL allows for building flexible queries based on metadata. UDFs encapsulate reusable data transformation logic. Stored procedures orchestrate the entire process. Options A and B lead to code duplication and are difficult to maintain. Option D can be inefficient for complex transformations. Option E pushes data quality issues to the BI layer, which is not ideal.
NEW QUESTION # 71
In data modeling for BI requirements, when is it preferable to use a flattened data set instead of a data model?
A. For situations requiring high data normalization
B. For scenarios necessitating extensive data transformations
C. For quick and simple data exploration
D. For complex data analysis needs
Answer: C
Explanation:
Flattened data sets are suitable for quick and simple data exploration due to their simplified structure, facilitating easy access and analysis.
NEW QUESTION # 72
You are investigating why a Snowflake data replication process between two regions is experiencing significant lag. You need to collect data to determine if the issue stems from network latency, insufficient warehouse resources in the target region, or data transformation bottlenecks. Select the data collection methods that will provide the MOST relevant insights.
A. Monitor the replication lag metrics (e.g., DATABASE REPLICATION_LAG, TABLE REPLICATION_LAG) exposed through Snowflake system functions and the web interface for both the source and target regions.
B. Analyze the query history in the target region to identify slow-running transformation queries that might be bottlenecking the replication process.
C. Restart the data replication process.
D. Run traceroute commands between the source and target regions to measure network latency.
E. Monitor the CPU utilization of the virtual machines running the Snowflake service in both regions.
Answer: A,B,D
Explanation:
Options A, B, and C provide specific data points relevant to the identified potential causes. Monitoring replication lag metrics (A) directly quantifies the lag. Traceroute (B) measures network latency. Analyzing query history (C) identifies transformation bottlenecks. Restarting the process (D) might temporarily resolve the issue but doesn't address the root cause. Snowflake manages the underlying infrastructure; therefore, monitoring VM CPU utilization (E) is not something that a data analyst has access to or is needed for the diagnostic in this case. The Snowflake service runs and manages the queries.
NEW QUESTION # 73
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