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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q146-Q151):

NEW QUESTION # 146
You are examining sales data for an e-commerce company in Snowflake. You notice a recurring dip in sales every Wednesday. The 'SALES' table contains columns: 'order_id', 'order date' , 'product_id', 'quantity', 'price'. Which of the following SQL queries and analytical techniques would be the MOST effective for diagnosing the reasons behind this weekly sales dip, assuming no external promotional activity always happens on Wednesdays?

A. Run a query to calculate the average daily sales for each day of the week and visualize the results to confirm the dip on Wednesdays. Use "DAYOFWEEK' function for this.
B. Check if there are any scheduled maintenance windows or system outages occurring on Wednesdays that might be affecting order processing. Use 'INFORMATION SCHEMA.LOAD HISTORY' to look for any loads failing on wednesdays
C. Review recent code deployments related to the e-commerce platform and identify if any changes were implemented on Tuesdays or Wednesdays that could be impacting sales.
D. Investigate shipping times by day of the week to determine if longer shipping times on orders placed earlier in the week are affecting Wednesday sales. Join ' SALES table with 'SHIPPING' table and calculate difference between 'order_date' and 'ship_date' .
E. Analyze sales patterns by 'product_id' and day of the week to see if specific product categories are driving the overall dip on Wednesdays. Use 'PIVOT' operator for cross-tabulation.

Answer: A,C,D,E

Explanation:
Options A, B, C, and E are the most relevant. Option A helps confirm the dip. Option B identifies product-specific patterns. Option C investigates a potential shipping-related cause. Option E explores potential software changes, while Option D focuses on a possible but less relevant cause of system maintenance.

NEW QUESTION # 147
You are responsible for collecting server log data from multiple geographically distributed data centers. The logs are generated at a high velocity and variety of formats (JSON, CSV, plain text). The requirement is to ensure minimal data loss and efficient ingestion into Snowflake, while also handling potential schema variations across different log sources. Which of the following is the MOST robust and scalable solution, considering potential schema drift and data volume?

A. Use a centralized file server to collect logs and then use Snowpipe with schema detection enabled on a single variant column in Snowflake.
B. Configure each data center to directly stream logs to Snowflake using the Snowflake JDBC driver.
C. Employ a distributed log aggregation system (e.g., Fluentd or Logstash) to standardize the log format and then use Snowpipe to ingest the data into Snowflake.
D. Utilize a message queue (e.g., Kafka) to collect logs from all data centers and create an external table pointing to the message queue. Use Snowflake streams to ingest the data from the message queue into Snowflake.
E. Write a custom Python script to pull logs from each data center, transform them into a consistent CSV format, and then upload the CSV files to Snowflake using Snowpipe.

Answer: C

Explanation:
A distributed log aggregation system (Fluentd/Logstash) is the best choice here. These systems are designed for handling high- velocity, varied log formats, and schema variations. They can buffer data to prevent data loss and transform the data into a consistent format before ingestion into Snowflake. Snowpipe provides efficient data loading from cloud storage. This combination provides scalability, reliability, and flexibility. Message queues require more configuration overhead and external tables can be slower for querying. Custom scripts are less scalable and harder to maintain. Direct streaming using JDBC is not recommended for high-volume data.

NEW QUESTION # 148
A financial institution uses Snowflake to store transaction data'. They observe an unexpected spike in fraudulent transactions during a specific weekend. The 'TRANSACTIONS table contains columns like 'transaction id', 'transaction date', 'amount', 'merchant_category', 'user_id', and 'is_fraudulent. Which of the following approaches, leveraging Snowflake's capabilities, would be MOST effective in diagnosing the potential causes of this fraud spike?

A. Join the 'TRANSACTIONS table with a table containing external threat intelligence data (e.g., IP address reputation, known fraudulent merchants) to identify transactions originating from suspicious sources.
B. Use a machine learning model (deployed as a Snowflake user-defined function) to re-score all transactions from the affected weekend based on historical patterns and identify potentially misclassified transactions.
C. Only check the transaction details, like amount and the users involved, and assume one of the users triggered those Fraud Trasanctions.
D. Analyze transaction patterns by 'merchant_category' and 'user_id' before, during, and after the spike to identify any unusual concentrations or emerging fraudulent schemes. Consider using 'QUALIFY' clause to filter top N merchant_category by transaction amount.
E. Create cohorts of users based on transaction behavior before the spike and compare their transaction patterns during the spike to identify anomalies within specific user groups. Use window functions like 'FIRST_VALUE to define cohorts.

Answer: A,B,D,E

Explanation:
Options A, B, C, and D provide a robust diagnostic approach. A uses machine learning for anomaly detection, B analyzes patterns by merchant category and user, C incorporates external threat intelligence, and D examines cohort behavior. Only checking specific transaction is not effective.

NEW QUESTION # 149
You need to create a UDF in Snowflake to mask personally identifiable information (PII) in a 'customers' table. The UDF should take a string as input (e.g., email address, phone number) and return a masked version. For email addresses, it should replace everything before the '@' symbol with ' For phone numbers (assuming a simple format like '123-456-7890'), it should mask all but the last four digits with 'X'. Which of the following UDF definitions is MOST efficient and correctly implements this logic using Snowflake's JavaScript UDF capabilities and appropriate error handling?

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

Answer: A

Explanation:
Option D is the most efficient and robust. It uses 'indexOf and regular expressions ('test()') for more reliable string matching, includes error handling using a 'try...catch' block, and correctly handles the different masking scenarios. The regex ensures that the phone number format is strictly validated before masking. Options A and B lack error handling and use less precise matching techniques (e.g., 'includes' which could lead to unintended behavior). Option C is similar to option D but utilizes javascript includes which is not reliable as indexof in finding exact position. Option E is a Python UDF, which, while valid, would be less performant than javascript UDF as specified in question.

NEW QUESTION # 150
How can automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?

A. They solely increase the complexity of dashboard management.
B. They limit the scalability of dashboards and reports.
C. Automated tasks ensure consistency and reduce manual effort.
D. Repeatable tasks hinder data updates in dashboards.

Answer: C

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.

NEW QUESTION # 151
......

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