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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q215-Q220):

NEW QUESTION # 215
Consider a scenario where you are building a dashboard to monitor the performance of a marketing campaign. The data includes daily ad spend, website conversions, and cost per acquisition (CPA). The stakeholders need to quickly assess whether the campaign is meeting its target CPA. What visualization type would be MOST appropriate to display the current CPA compared to the target CPA, providing a clear and concise view of performance?

A. A Pie chart showing CPA percentage against target CPA percentage.
B. A scatter plot showing the relationship between ad spend and CPA.
C. A line chart showing the trend of CPA over time.
D. A gauge chart displaying the current CPA and the target CPA, with color-coded zones indicating performance levels.
E. A bar chart comparing the average CPA to the target CPA.

Answer: D

Explanation:
A gauge chart is specifically designed to display a single value (the current CPA) in relation to a target value (the target CPA). The color-coded zones provide an immediate indication of whether the campaign is performing well, needs improvement, or is failing. Line charts show trends, bar charts compare averages, and scatter plots show relationships. Pie chart showing CPA percentage against target CPA percentage do not accurately show CPA against target CPA, for better visualization gauge charts would be the preffered option

NEW QUESTION # 216
You are tasked with analyzing website clickstream data stored in a Snowflake table called 'clickstream_events'. Each row represents a click event and contains a 'session_id' , and a 'properties' column of type VARIANT that stores key-value pairs related to the event (e.g., '(page': '[product/123', 'element': You need to extract the 'page' and 'element' values from the 'properties' column and identify the most common 'page'-'element' combinations for each 'session_id'. Furthermore you need to limit the results of your data to the top 5 pages element pair. How can this task be accomplished using Snowflake table functions and analytical functions?

A. Extract 'page' and 'element' using 'properties:page' and 'properties:element' directly in the SELECT statement, then use GROUP BY 'session_id', 'page', 'element' and QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
B. Use multiple LATERAL FLATTEN calls, one for 'page' and one for 'element', then JOIN the results on and use QUALIFY ROW_NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
C. First create a view that flattens the JSON column using LATERAL FLATTEN, then select from this view to perform the group by and ranking operations.
D. Create a UDF to parse the 'properties' VARIANT and return a table with 'page' and 'element columns, then JOIN this UDF's output with the original table and use QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
E. Use LATERAL FLATTEN to extract the keys and values from the 'properties' column, then use GROUP BY 'session_id', 'key', 'value' and COUNT( ) to find the most frequent combinations.

Answer: A

Explanation:
Option C is the most efficient and Snowflake-idiomatic way to achieve this. Directly accessing 'properties:page' and properties:element' is more performant than using LATERAL FLATTEN when you know the specific keys you need. The QUALIFY clause, combined with ROW NUMBER(), efficiently filters the results to the top 5 combinations per session. LATERAL FLATTEN is generally used when you need to iterate over an array within the VARIANT, not when you're extracting specific key-value pairs. UDF introduces extra overhead.

NEW QUESTION # 217
You are analyzing customer churn for a subscription-based service. You have a table 'SUBSCRIPTIONS' with columns: 'CUSTOMER_ID, 'START_DATE', 'END_DATE', 'SUBSCRIPTION TYPE, and 'REVENUE'. You want to classify customers who are likely to churn based on their past subscription behavior. Which Snowflake SQL code snippet is MOST efficient for calculating the number of months each customer was subscribed and identifying those who subscribed for less than 3 months as potential churn candidates?

Answer: D

Explanation:
Option D is the most efficient. It calculates the 'MONTHS SUBSCRIBED directly in the SELECT statement and filters the results in the 'WHERE clause. Option A will produce an error because you cannot refer to an alias (MONTHS_SUBSCRIBED) in the same 'WHERE clause where it's defined. Option B creates a temporary table, which is unnecessary overhead for this simple calculation. Option C uses HAVING& which is used for filtering aggregated results, not individual rows before aggregation. Snowflake does not natively support MONTHS_BETWEEN, thus option E would result in an error.

NEW QUESTION # 218
You are tasked with ingesting data from a variety of external sources (JSON, CSV, Parquet) into Snowflake to build a unified customer profile. The sources contain inconsistencies in data types, null handling, and naming conventions. Your goal is to create a robust ingestion pipeline that identifies data quality issues and prepares the data for analysis. Which combination of Snowflake features and approaches would be most effective for achieving this?

A. Utilize Dynamic Tables with appropriate transformation logic defined to handle inconsistencies and maintain data quality during ingestion. Leverage Snowflake's data governance features in conjunction with Dynamic Tables for comprehensive data management
B. Use Snowpipe with schema evolution enabled and rely solely on Snowflake's automatic data type conversion. Implement data quality checks in downstream views.
C. Leverage Snowflake's data governance features with data profiling, implement data quality rules in a data catalog, and use a combination of Snowpipe and COPY INTO with data transformations within the COPY INTO statement.
D. Build a custom data ingestion application using the Snowflake Connector for Python, implementing complex data transformations and quality checks within the application before loading the data into Snowflake. Store raw data as is for future use.
E. Employ a third-party ETL tool integrated with Snowflake, configure data cleansing and transformation rules within the ETL tool, and load the transformed data into Snowflake using its optimized connectors.

Answer: A,C,D,E

Explanation:
Options B, C, D, and E represent robust approaches. B combines data governance and built-in transformations. C allows for custom logic using the Python connector. D uses a specialized ETL tool. E leverages Dynamic Tables for automated transformations and data quality maintenance. A is insufficient as it relies too heavily on automatic conversion and delays quality checks. The best approach would involve data profiling to understand the data, implementing data quality rules at the point of ingestion or through dynamic tables, and leveraging a transformation approach suited to the complexity of the transformations.

NEW QUESTION # 219
A healthcare provider is investigating patient readmission rates within 30 days of discharge. They suspect a correlation between patient demographics (age, gender, location) and readmission. You have the following tables: 'PATIENTS': 'patient_id', 'age', 'gender' , 'zip_code' 'ADMISSIONS': 'admission_id' , 'patient_id' , 'admission_date', 'discharge_date' Which of the following approaches would be MOST effective to identify patient demographics significantly correlated with higher readmission rates within 30 days? (Select TWO)

A. Develop a complex SQL query to directly identify and list all patients who have been readmitted more than twice in the last year, irrespective of their demographics.
B. Create a Snowflake user-defined function (UDF) in Python to perform a complex machine learning model directly on the data to predict readmissions based on demographics, without any initial exploratory data analysis.
C. Calculate the overall readmission rate and compare it to the readmission rates for different demographic groups using Chi-Square tests or similar statistical methods to assess statistical significance.
D. Use correlation coefficients (e.g., Pearson, Spearman) to directly measure the linear association between demographics and the binary readmission outcome (1 -readmitted, 0=not readmitted).
E. Implement a cohort analysis to track patient readmission rates over time for different demographic segments and visualize the trends using Snowflake's data visualization capabilities (if integrated) or export the data to a BI tool.

Answer: C,E

Explanation:
Options A and D are the most effective. A suggests using statistical tests (Chi-square) which can identify statistically significant differences in readmission rates across demographic groups. D proposes cohort analysis, enabling the tracking and visualization of readmission trends for different demographics over time, allowing for the identification of segments with consistently high readmission rates. Option B is not optimal as it jumps directly into complex modeling without initial EDA. Option C, while valid, doesn't handle categorical variables (gender, zip_code) well and might miss non-linear relationships. Option E, identifying high-frequency readmitters, is helpful but doesn't directly address the relationship with demographics.

NEW QUESTION # 220
......

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