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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
You have a Snowflake table named 'customer transactionS with columns: 'customer id', 'transaction date', 'transaction amount, and product_categorV. You need to identify customers who have made purchases in more than three different product categories within the last 30 days. Which of the following Snowflake SQL queries is the MOST efficient and accurate way to achieve this, considering the large size of the table?

A. Option A
B. Option C
C. Option E
D. Option B
E. Option D

Answer: B

Explanation:
Option C is the most efficient. It directly groups by 'customer_id' and uses the 'HAVING' clause to filter customers who have purchased from more than three distinct product categories within the specified date range. Option A is functionally correct but less concise. Option B uses an inefficient subquery. Option D returns all customer IDs who purchased from any product category within the timeframe. Option E attempts to use window functions and ARRAY AGG, which is unnecessarily complex and less performant for this task.

NEW QUESTION # 41
You are tasked with enriching your company's customer transaction data with external economic indicators (e.g., unemployment rate, GDP) obtained from a Snowflake Marketplace data provider. The transaction data resides in a table 'TRANSACTIONS' with columns 'TRANSACTION (INT), 'TRANSACTION DATE (DATE), and (VARCHAR). The economic indicators data, obtained from the Marketplace, is available in a table 'ECONOMIC DATA' with columns 'DATE (DATE), ZIP_CODE (VARCHAR), 'UNEMPLOYMENT RATE (NUMBER), and 'GDP' (NUMBER). Due to data quality issues, some zip codes in both tables are missing or malformed. You need to create a view that efficiently joins these two tables, handles missing or malformed zip codes, and provides the transaction data enriched with the economic indicators. Which of the following approaches is the MOST robust and efficient way to create this enriched view, minimizing data loss and maximizing data quality?

A. Create a view that first filters out all rows with missing or malformed zip codes from both 'TRANSACTIONS' and 'ECONOMIC DATA' using 'WHERE clauses and regular expressions to validate the zip code format. Then, perform an ' INNER JOIN' between the filtered datasets on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE.
B. Create a view using a 'LEFT OUTER JOIN' between 'TRANSACTIONS and ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION_DATE =ECONOMIC_DATA.DATE' and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE'. Additionally, use the function to handle malformed zip codes and the 'NVL' function to replace missing or malformed zip codes with a default zip code (e.g., '00000') for joining purposes. Also include a new column "ENRICHMENT SUCCESS' that flag indicates that the join was successful or whether data was enriched using the default zip code.
C. Create a stored procedure that iterates through each transaction in 'TRANSACTIONS' , attempts to find a matching economic data record in ECONOMIC_DATA' based on date and zip code, and updates a new 'TRANSACTIONS_ENRICHED table with the economic indicators. Handles missing zipcodes by setting the 'UNEMPLOYMENT RATE' and 'GDP ' to 0 for any record in transaction which zip code is missing.
D. Create a Snowflake Task that runs daily to update a materialized view that joins 'TRANSACTIONS' and 'ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE , handling missing zip codes by skipping those records entirely.
E. Create a view that performs a simple 'JOIN' between 'TRANSACTIONS' and 'ECONOMIC DATA' on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC_DATDATE and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE. This approach ignores missing or malformed zip codes.

Answer: B

Explanation:
Option C provides the most robust and efficient solution. Using 'LEFT OUTER JOIN' ensures that all transactions are included in the view, even if there is no matching economic data. 'TRY TO NUMBER handles malformed zip codes gracefully by converting valid zip codes to numbers and returning NULL for invalid ones, preventing errors. 'NVL' replaces NULL zip codes (either originally missing or resulting from TRY_TO_NUMBER) with a default value, allowing the join to proceed using a fallback. Adding the 'ENRICHMENT_SUCCESS' flag provides transparency about which records were enriched using the default zip code, enabling users to assess the reliability of the enriched data. Option A is inadequate because it ignores missing or malformed zip codes, leading to data loss. Option B is inefficient and not scalable due to row-by-row processing. Option D discards records with missing or malformed zip codes, resulting in significant data loss. Option E does not specifically handle data quality issues related to missing or malformed zip codes. Further the use of Tasks and materialized views, while increasing performance, doesn't necessarily address the issue of data quality.

NEW QUESTION # 42
What actions can be taken when responding to processing failures in data processing solutions?
(Select all that apply)

A. Identify the root cause of failure
B. Resume processing from the point of failure
C. Log error details for analysis
D. Restart the entire data processing flow

Answer: A,B,C

Explanation:
Responding to processing failures involves logging error details, identifying the root cause, and resuming processing from the failure point.

NEW QUESTION # 43
Which actions are pertinent in identifying demographics and relationships during diagnostic analysis? (Select all that apply)

A. Analyzing statistical trends
B. Examining anomalies in isolation
C. Ignoring data relationships for focused analysis
D. Collecting related data

Answer: A,D

Explanation:
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships during diagnostic analysis.

NEW QUESTION # 44
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

A. Using a traditional ETL tool to extract, transform (calculate rolling average), and load the data into Snowflake in 15-minute intervals.
B. Using a scheduled task to query the API every minute and store the data directly into a Snowflake table with a materialized view calculating the rolling average.
C. Employing a stream processing framework (e.g., Apache Kafka) to ingest the data, perform the rolling average calculation using a tumbling window, and load the aggregated results into Snowflake.
D. Leveraging Snowflake's dynamic data masking and data classification capabilities to maintain data security and compliance while adhering to real-time data ingestion.
E. Storing the raw data into Snowflake using Snowpipe in micro-batches and creating a VIEW that performs the rolling average calculation on-demand.

Answer: C

Explanation:
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.

NEW QUESTION # 45
......

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