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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?

A. Data shares only work with Snowflake-provided datasets.
B. They restrict access to external data.
C. They limit the types of data that can be joined.
D. Data shares facilitate secure data exchange between parties.

Answer: D

Explanation:
Data shares enable secure data exchange, allowing joining external data with existing datasets.

NEW QUESTION # 116
What complexities might arise when identifying and resolving data import errors in Snowflake?
(Select all that apply)

A. Analyzing error logs for resolution
B. Identifying error sources
C. Handling only specific error types
D. Resolving data inconsistencies

Answer: A,B,D

Explanation:
Identifying and resolving data import errors involves challenges related to identifying error sources, resolving inconsistencies, and analyzing logs for resolution, which might be complex depending on the nature of the errors.

NEW QUESTION # 117
You are tasked with migrating data from an existing relational database to Snowflake. The database contains a 'Customers' table (CustomerlD, Name, City, State) and an 'OrderS table (OrderlD, CustomerlD, OrderDate, TotalAmount). A critical requirement is to maintain data integrity and quickly identify any orphaned records after the initial load. Which approach provides the MOST efficient and robust method to identify orphaned 'Orders' (orders where the CustomerlD does not exist in the 'Customers' table) in Snowflake, considering large data volumes and performance?

A. Perform a full outer join between 'Customers' and 'Orders' tables and filter for records where 'Customers.CustomerlD' is NULL. Use a 'NOT EXISTS' subquery:
B. Use a 'LEFT OUTER JOIN' between 'OrderS and 'Customers' tables and filter for records where 'Customers.CustomerlD is NULL.
C. Load the 'Customers' table into a temporary stage, then use a stored procedure with iterative cursor-based logic to compare each 'OrderlD' against the staged customer data.
D. SELECT OrderlD FROM Orders WHERE NOT EXISTS (SELECT I FROM Customers WHERE Customers. CustomerID = Orders. CustomerID);
E. Load 'Customers' and 'Orders' to separate Snowflake tables. Then using a UDF, implement a fuzzy match using Levenshtein distance between 'Orders.CustomerlD and 'Customers.CustomerlD , identifying potential mismatches for manual review.

Answer: B

Explanation:
A 'LEFT OUTER JOIN' (or 'LEFT JOIN') is the most efficient and direct method for identifying orphaned records in the 'Orders' table. By joining 'Orders' to 'Customers' on 'CustomerlD, and then filtering for records where 'Customers.CustomerlD' is NULL, you directly isolate the orders with no corresponding customer. 'NOT EXISTS' (option B) can also work, but it is often less performant than a left join with a NULL check, especially with large datasets. A full outer join (option A) would show both orphaned orders and orphaned customers, which isn't the specific goal. Using a stored procedure with a cursor (option D) is highly inefficient and not scalable. The UDF fuzzy match approach (option E) is unnecessarily complex and not designed for direct identification of orphaned foreign keys.

NEW QUESTION # 118
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?

Answer: E

Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.

NEW QUESTION # 119
You need to create a dashboard for a logistics company to track delivery performance. The dashboard should display the following information: (1) Total number of deliveries per day, (2) Percentage of deliveries completed on time, (3) Average delivery time per city, (4) Number of deliveries exceeding the SLA (Service Level Agreement) by more than 1 hour. Which of the following chart combinations would be MOST effective to display this information in a clear and concise manner?

A. Time series chart for total deliveries per day, gauge chart for percentage of on-time deliveries, a heat grid showing city vs average delivery time, and a bullet chart for deliveries exceeding SLA target.
B. Bar chart for total deliveries per day, scorecard for percentage of on-time deliveries, geographical map with color-coded cities for average delivery time, and a table showing deliveries exceeding SLA.
C. Time series chart for total deliveries per day, scorecard for percentage of on-time deliveries, geographical map with color-coded cities for average delivery time, and a table showing deliveries exceeding SLA.
D. Area chart for total deliveries per day, scorecard for percentage of on-time deliveries, geographical map with color-coded cities for average delivery time, and a bar chart for deliveries exceeding SLA.
E. Line chart for total deliveries per day, pie chart for percentage of on-time deliveries, bar chart for average delivery time per city, and a table showing deliveries exceeding SLA

Answer: C

Explanation:
A Time series chart is best for showing trends over time (total deliveries per day). A scorecard provides a concise view of a key metric (percentage of on-time deliveries). A geographical map with color-coded cities effectively visualizes location-based data (average delivery time per city). A table provides detailed information on specific instances (deliveries exceeding SLA). Pie Charts are less ideal for percentage since it can be achieved better using Scorecard, heat grid not the best choice when geo is involved and it is less readable.

NEW QUESTION # 120
......

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Valid DAA-C01 Test Discount & DAA-C01 Exam Introduction

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q115-Q120):

Newest DAA-C01 Preparation Engine: SnowPro Advanced: Data Analyst Certification Exam Exhibit Hhigh-effective Exam Dumps - DumpStillValid

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