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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
You are building a dashboard to monitor the performance of a Snowflake data pipeline. This pipeline ingests data from various sources, transforms it, and loads it into target tables. You want to visualize the overall pipeline latency, including the time spent in each stage (ingestion, transformation, loading). You have access to event logs that capture the start and end timestamps for each stage of each pipeline run. The logs are stored in a Snowflake table named 'PIPELINE LOGS' with columns: 'PIPELINE RUN (VARCHAR), 'STAGE_NAME' (VARCHAR), 'START_TIMESTAMP' (TIMESTAMP_NU), 'END_TIMESTAMP (TIMESTAMP_NTZ). Which visualization type and query construct provides the MOST effective way to visualize the latency of each stage within each pipeline run, allowing for easy identification of bottlenecks?

A. A box plot visualizing the distribution of latencies for each stage, generated using a query with window functions to calculate percentiles and outliers.
B. A heatmap showing correlation between start_timestamp and end_timestamp for each pipeline run for all stages.
C. A bar chart showing the average latency for each stage, calculated using the aggregate function and grouping by 'STAGE_NAME'.
D. A Gantt chart displaying the start and end times of each stage for each pipeline run, created using a query that calculates the duration of each stage using 'TIMESTAMPDIFF()'.
E. A line chart showing the total latency of each pipeline run over time, calculated using the 'SUM()' aggregate function and grouping by and date.

Answer: D

Explanation:
A Gantt chart (C) is the most effective visualization for this scenario. It directly shows the start and end times of each stage within each pipeline run, making it easy to visually identify bottlenecks and understand the overall timeline. The query would need to calculate the duration of each stage using 'TIMESTAMPDIFF()' to determine the length of each bar in the Gantt chart. The other options provide aggregated summaries (A, D) or distributions (B) that don't directly show the temporal relationship between stages within each pipeline run. Heatmap is not so useful here.

NEW QUESTION # 163
You have Parquet files containing customer order data in an external stage. You need to create a secure view in Snowflake that filters the data based on user roles. Only users with the 'SALES REP' role should see orders from their assigned region, while 'MANAGER' roles should see all data'. Assuming you have set up appropriate role hierarchies, how would you implement this using row-level security policies with Parquet external tables?

A. Create a secure view that queries the Parquet files directly using 'JSON EXTRACT PATH TEXT to access the region information. Apply a JOIN operation with a role table to filter the data based on the user's role.
B. Create a secure view that queries the external table. Then, define a row access policy that filters the data based on the user role and the region, and apply the policy to the secure view. The policy should use to determine the appropriate filtering criteria. Delegate the ownership to 'ACCOUNTADMIN'.
C. Create an external table with a schema defined according to the Parquet files. Define a masking policy that hides the sensitive information for users without proper roles. Create a standard view on top of the external table. Apply the masking policy to a column inside view.
D. Create a secure view that directly queries the Parquet files using to access the region information. Apply a WHERE clause in the view definition using to filter based on the user's role. No RLS policy needed.
E. Create a standard view querying the external table. Create a row access policy that filters the data based on the user role and the region. Apply the policy to the external table. Delegate the ownership to 'ACCOUNTADMIN'.

Answer: B

Explanation:
Row Access Policies (RAP) are designed for Row Level Security, and they are applied to tables or views. Since the data is in an external table accessed through a view, the policy should be attached to the secure view. The ' function is used to determine the user's current role within the policy's logic. Secure Views protect the underlying data and policy logic from unauthorized access. Delegating ownership to 'ACCOUNTADMIW is not strictly required but a good security practice. Masking policies aren't the correct choice here as it is used to hide data from the user.

NEW QUESTION # 164
A data analyst is performing an exploratory analysis on sales data and observes a highly skewed distribution for the 'sales_amount' column. Which of the following data transformation techniques are appropriate to mitigate the impact of the skewness and make the data more suitable for modeling and analysis? (Select all that apply)

A. Log Transformation
B. Winsorization
C. Box-Cox Transformation
D. Standardization
E. Min-Max Scaling

Answer: A,B,C

Explanation:
Log transformation and Box-Cox transformation are common techniques used to reduce skewness in data. Winsorization helps to reduce the impact of outliers, which can contribute to skewness. Standardization and Min-Max scaling are primarily used for feature scaling and do not directly address skewness.

NEW QUESTION # 165
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.

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

Answer: B

Explanation:
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.

NEW QUESTION # 166
You have a table 'PRODUCTS' with a 'PRICE' column stored as VARCHAR. Some values in this column are valid numerical strings (e.g., '12.99'), while others contain invalid characters (e.g., '12.99USD', 'N/A'). You need to calculate the average price of all valid products. Which of the following approaches ensures that you only consider valid numeric values and handles potential errors effectively? Select all that apply.

Answer: B,C

Explanation:
Option B is correct because it uses a regular expression to explicitly check if the PRICE column contains only valid numeric characters (digits and an optional decimal point) before attempting to cast it to a DECIMAL. 'REGEXP LIKE' ensures that non-numeric values are filtered out, preventing errors during casting, and then 'CAST is used since it is validated by the regular expression check. Option D is also correct because it uses 'CASE statement with 'IS DECIMAL' function that is a user defined function (UDF) for checking if a string can be converted to decimal, and if True, it casts the PRICE to DECIMAL; otherwise, it assigns NULL. The AVG function automatically ignores NULL values, ensuring that only valid numeric values are considered. Option A won't work as DECIMAL' function is not standard, so you will need to create user defined function. 'TO_NUMBER and 'TO_DECIMAL' throws error if it cannot parse which is not ideal. Option E, throws error if column PRICE cannot be converted, where as 'TRY TO DECIMAL' will not throw the error and return NULL.

NEW QUESTION # 167
......

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