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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
Why would an INFER_SCHEMA table function be used?

A. To detect the metadata schema from a table cell that contains JSON data
B. To detect the file metadata schema from staged data files that contain semi-structured data
C. To retrieve the column definitions from a previously-loaded database table
D. To detect the file metadata schema from staged data files that contain unstructured data

Answer: B

Explanation:
The INFER_SCHEMA function is a powerful tool in the Snowflake ingestion toolkit, specifically designed to bridge the gap between semi-structured data and relational tables. In modern data engineering, files like Parquet, Avro, and ORC are "self-describing," meaning they contain their own schema definitions (field names and data types) within the file header.
Before INFER_SCHEMA existed, a Data Analyst had to manually inspect a Parquet file, identify the keys, determine their data types, and then manually write a CREATE TABLE statement with the matching columns. INFER_SCHEMA automates this by scanning a set of files in a stage and returning a result set that describes the detected schema. This allows for the dynamic creation of tables that perfectly match the incoming data files.
Evaluating the Options:
* Option A is incorrect because "unstructured data" (like PDFs or Images) does not have a schema that can be "inferred" in a relational sense.
* Option C is incorrect because INFER_SCHEMA operates on files in a Stage, not on data already sitting in a table cell. To parse JSON in a table, you would use functions like FLATTEN or simple path notation.
* Option D is incorrect because the DESCRIBE TABLE or GET_DDL commands are used to retrieve definitions for existing tables.
* Option B is the Correct answer. It specifically identifies the role of the function: detecting metadata (schema) from staged semi-structured files. This function is often used in conjunction with GENERATE_COLUMN_DESCRIPTION and CREATE TABLE ... USING TEMPLATE to fully automate the schema evolution and ingestion process.

NEW QUESTION # 50
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?

A. IoT sensor data containing temperature readings.
B. Database containing HR employee data.
C. Salesforce data containing customer interactions and sales opportunities.
D. Social media activity data ingested via a third-party API and stored in a relational database.
E. Website clickstream data stored in AWS S3 buckets in Parquet format.

Answer: C,D,E

Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.

NEW QUESTION # 51
You are analyzing website traffic data in Snowflake and notice a sudden drop in page views from a specific country (Country A) starting last month. You have access to the 'WEBSITE TRAFFIC' table with columns: 'date' , 'country', 'page_viewS, 'device_type'. Which of the following queries and techniques would be MOST effective in identifying the potential cause of this anomaly?

A. Execute 'SELECT FROM WEBSITE_TRAFFIC WHERE country = 'Country A' AND date DATEADD(month, -1, CURRENT DATE());' and manually inspect the data for suspicious patterns.
B. Join the 'WEBSITE_TRAFFIC' table with a table containing marketing campaign data (MARKETING CAMPAIGNS') on 'date' and 'country' to see if any marketing campaigns were paused or modified in Country A around the time of the drop. Consider using 'LEFT JOIN' to not lose traffic data.
C. Use a statistical anomaly detection function (e.g., moving average) on the 'page_views' for Country A and compare against other countries to identify if the drop is specific to Country A. Consider using 'LAG' function with 'OVER' clause to calculate the moving average.
D. Analyze 'page_viewS by 'device_type' for Country A before and after the drop to see if the drop is concentrated in a specific device type (e.g., mobile, desktop). Use 'CASE statement within the 'GROUP to categorize time periods.
E. Run a simple 'SELECT FROM WEBSITE_TRAFFIC WHERE country = 'Country A' AND date DATEADD(month, -3, GROUP BY date ORDER BY date;' to visualize the trend and confirm the drop.

Answer: B,C,D

Explanation:
Options B, C, and D are the most effective. B uses statistical methods to identify the anomaly, C investigates potential external factors (marketing campaigns), and D explores internal segments (device types). Option A is a basic check but doesn't identify causes. Option E is not scalable and inefficient for large datasets. Using a combination of statistical analysis, external data integration, and segmentation provides a comprehensive diagnostic approach.

NEW QUESTION # 52
You are tasked with creating a stored procedure in Snowflake to perform data cleansing on a table named 'CUSTOMER DATA'. The procedure should: 1) Remove rows where the 'EMAIL' column is NULL or empty. 2) Standardize the 'PHONE NUMBER' column by removing all non-numeric characters and ensuring it's exactly 10 digits long. 3) Return the number of rows removed due to invalid emails and the number of rows modified due to phone number standardization. Assume the table already exists and contains columns 'CUSTOMER (INT), (VARCHAR), and 'PHONE NUMBER (VARCHAR). Which of the following code snippets correctly implements this stored procedure? The procedure should use exception handling to gracefully handle errors, returning -1 for both counts if any error occurs.

A.
B.
C.
D. None of the above.
E.

Answer: A

Explanation:
Option A is correct because it uses SQL to perform the data cleansing tasks, correctly utilizes 'SQLROWCOUNT' to capture the number of affected rows, and returns the results as a VARIANT OBJECT. It also includes proper exception handling. Options B, C, and D have errors in syntax or logic regarding return types, variable declaration, or how to retrieve row counts. Specifically, using Javascript or returning an ARRAY/TABLE when VARIANT is more flexible in this scenario.

NEW QUESTION # 53
How do exploratory ad-hoc analyses differ from routine analysis?

A. Ad-hoc analyses rely heavily on predefined queries.
B. Ad-hoc analyses focus on anomalies and established trends.
C. Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data.
D. They involve querying known patterns without exploring further.

Answer: C

Explanation:
Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data beyond predefined queries.

NEW QUESTION # 54
......

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