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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

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

2. You are working with a 'product catalog' table that contains 'product id', 'product_name', 'category', and 'price'. You notice that some product names have leading and trailing whitespace, inconsistent capitalization, and special characters. Which combination of Snowflake functions and techniques would provide the MOST comprehensive solution for cleaning the 'product_name' column to ensure consistency and accuracy in your analysis? (Select all that apply)

A) Create a Snowflake UDF (User-Defined Function) that encapsulates all the cleaning steps for reuse and better code organization.
B) Use or to standardize capitalization.
C) Manually review and update each product name individually within the table using UPDATE statements.
D) Use 'TRIM()' to remove leading and trailing whitespace.
E) Use ' REPLACE(Y in a nested fashion, or regular expression functions like 'REGEXP REPLACE()' , to remove or replace special characters.

3. You are tasked with creating a dashboard in Snowsight to visualize sales data'. You have a table 'SALES DATA' with columns 'ORDER_DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'SALES_AMOUNT (NUMBER), and 'REGION' (VARCHAR). The business requirements include the following: 1. Display total sales amount by product category in a pie chart. 2. Display a table showing sales amount for each region for a user-selected date range. 3. Allow the user to filter both visualizations by a specific region.
Which of the following approaches would BEST satisfy these requirements using Snowsight dashboards and features?

A) Create two separate charts: a pie chart for product category sales and a table for regional sales. Use the same filter on the dashboard for region, and manually enter the date range in the SQL query for the table chart.
B) Create a single Snowsight dashboard with a Python chart for product category sales, querying data using Snowflake Connector, and a table showing regional sales using SQL query. No dashboard variables are needed, as the Python script handles all filtering.
C) Create a single Snowsight dashboard with two charts: a pie chart showing total sales by product category using the query 'SELECT PRODUCT_CATEGORY, SUM(SALES AMOUNT) FROM SALES DATA WHERE REGION = $REGION GROUP BY PRODUCT_CATEGORY, and a table showing regional sales using the query 'SELECT REGION, FROM SALES_DATA WHERE ORDER_DATE BETWEEN $START_DATE AND $END_DATE AND REGION = $REGION GROUP BY REGION'. Define three dashboard variables: 'REGION' (Dropdown), 'START DATE (Date), and 'END DATE (Date).
D) Create two separate dashboards: one for the pie chart and another for the table. Use a global session variable to store the selected region and date range, and access it in the SQL queries for both dashboards.
E) Create a view with all calculations of the total sale amount, grouping by product category and region. Then create the dashboard with charts based off of this view. This will allow for easier modification if the business requirements change.

4. You have a table named 'event_data' that tracks user activities. The table contains 'event_id' (INT), 'user _ id' (INT), (TIMESTAMP NTZ), 'event_type' (VARCHAR), and 'event_details' (VARIANT). The table is partitioned by Performance on queries filtering by both 'event_type' and a specific date range on is slow You suspect inefficient partition pruning and JSON parsing as potential bottlenecks. Which combination of actions will most effectively address these performance issues?

A) Change the partition key to 'event_type' and create a table function to query sevent_detailss.
B) Create a materialized view partitioned by and clustered by 'event_type' , pre-extracting relevant fields from 'event_detailS into separate columns.
C) Add a masking policy on the 'event_details' column and recluster the table by 'user_id'.
D) Create a view that extracts specific fields from the 'event_details' column into separate columns and add a secondary index on 'event_type' .
E) Create a temporary table containing the results and then performing a Merge operation.

5. You are tasked with loading data from an S3 bucket into a Snowflake table named 'SALES DATA'. The data is in CSV format, compressed with gzip, and contains a header row The S3 bucket requires AWS IAM role authentication. The 'SALES DATA' table already exists, and you want to use a named stage for this ingestion process. Which of the following steps are necessary to successfully load the data, minimizing administrative overhead?

A) Create a new IAM role in AWS with access to the S3 bucket, then create a Snowflake storage integration object referencing that role's ARN and the S3 bucket's URL.
B) Ensure the S3 bucket has public read access; Snowflake's COPY INTO command will handle decompression and data loading without further configuration.
C) Create an external function to read the data from S3 and then insert it into the table, as Snowflake cannot directly read gzipped CSV files from S3.
D) Create a Snowflake stage object that references the storage integration, the S3 bucket URL, and specifies the file format (CSV with gzip compression and header skip). Use the 'COPY INTO' command referencing the stage.
E) Grant usage privilege on the storage integration to the role performing the data load. Ensure the user loading data has access to the Snowflake stage and the ' INSERT privilege on the table.

Solutions: