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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
You are tasked with creating a dashboard to visualize customer churn. You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID, 'JOIN DATE, 'LAST ACTIVE DATE, 'REVENUE', and CHURNED' (BOOLEAN). You want to present a cohort analysis showing the retention rate of customers over time, grouped by their join month. Which of the following approaches using SQL and visualization techniques is the MOST effective for creating this cohort analysis visualization for a dashboard using only snowflake?

A. Create a stored procedure that iterates through each join month, calculates the retention rate for each subsequent month, and stores the results in a temporary table. Then, use a charting library integrated with your dashboard to visualize the data from the temporary table as a heatmap or retention table.
B. Export the data to a BI tool to make a visualization.
C. Create a series of SQL queries, one for each cohort, to calculate the retention rate for each subsequent month. Combine the results from these queries in your dashboard and display them using a line chart.
D. Create a single SQL query that calculates the retention rate for each cohort (join month) using window functions to count active customers in each subsequent month. Then, display the results in a bar chart showing the retention rate for each cohort over time.
E. Use a sequence to generate a series of dates representing the months since joining, then use conditional aggregation to calculate the number of customers retained in each month for each cohort. Visualize this data using a line chart or heatmap.

Answer: E

Explanation:
Option C is the most efficient and scalable. Using a sequence and conditional aggregation allows calculating the retention rate for each cohort in a single SQL query. This avoids the overhead of stored procedures (B) or multiple queries (D). A line chart or heatmap provides an effective visualization of cohort retention over time. Option A can become complex to manage, especially when handling larger datasets. Finally, exporting the data for external visualization defeats the purpose of Snowflake analysis. Sequences and conditionals are preferred as Snowflake doesn't natively support loops like stored procedures for cohort analysis.

NEW QUESTION # 125
A large fact table is partitioned by and clustered by 'customer _ id'. The table has the following columns: 'customer_id', and 'transaction_amount'. You need to optimize queries that frequently filter on a specific range of 'transaction_date' and then aggregate by 'customer _ id'. Given the existing partitioning and clustering, which of the following strategies will BEST improve query performance related to partition pruning and clustering?

A. Add a secondary index on the 'transaction_date' column.
B. Recluster the table frequently using 'ALTER TABLE fact_transactions RECLUSTER;'
C. Create a new table partitioned by and clustered by 'customer_id' and migrate data. Drop the Original Table.
D. No further optimization is needed, the existing partitioning and clustering are sufficient.
E. Create a materialized view that pre-aggregates 'transaction_amount' by 'customer_id' and 'transaction_date' .

Answer: E

Explanation:
Option B is the best strategy. Creating a materialized view that pre-aggregates the data by and 'transaction_date' addresses both aspects:Partition pruning is naturally leveraged because the materialized view will store aggregated data, allowing queries filtering on 'transaction_date' to use partition pruning during refresh and query. Clustering helps because the data within each partition (date) is clustered by 'customer_id' , making aggregations by customer efficient. Option A might not provide sufficient performance improvement if the aggregation by customer is still slow. Option C will improve query performance marginally but is not a good option with partition pruning, because the data is already partition on date. Option D reclustering too frequently can be costly and may not always result in significant performance gains. Option E can be a costly operation and also data migration may be hectic. Thus the best is to have materialized view.

NEW QUESTION # 126
You are designing a data warehouse for a retail company. The "SALES table stores transaction data and includes columns like 'TRANSACTION ID', 'PRODUCT ID', 'CUSTOMER ID, 'SALE DATE', and 'SALE AMOUNT'. The 'PRODUCT ID' references the 'PRODUCTS table, and 'CUSTOMER references the 'CUSTOMERS' table. Which of the following strategies represent the MOST optimal approach to define primary keys in this scenario, considering Snowflake's best practices and the need for efficient query performance, assuming 'TRANSACTION ID' is globally unique?

A. Define as the primary key on the 'SALES' table. Do not define primary keys on 'PRODUCTS' or 'CUSTOMERS'
B. Define ' TRANSACTION_ID' as the primary key on the 'SALES' table. Define 'PRODUCT_ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID as the primary key on the 'CUSTOMERS table. Then, create unique indexes on 'PRODUCT ID in 'SALES referencing 'PRODUCTS and in referencing 'CUSTOMERS'.
C. Do not define primary keys on any of the tables. Rely solely on Snowflake's internal optimizations.
D. Define 'TRANSACTION as the primary key on the 'SALES' table, 'PRODUCT ID as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID' as the primary key on the 'CUSTOMERS' table.
E. Define a composite primary key on the 'SALES' table consisting of 'TRANSACTION D', 'PRODUCT ID, and 'CUSTOMER ID. Define 'PRODUCT ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER_ID' as the primary key on the 'CUSTOMERS' table.

Answer: D

Explanation:
While Snowflake does not enforce primary key constraints, defining them provides valuable metadata for the query optimizer. Since TRANSACTION_ID' is unique in 'SALES', it is a suitable primary key. Defining primary keys on 'PRODUCTS' and 'CUSTOMERS' is also appropriate for their respective tables. Creating unique indexes is redundant if primary keys are defined. Skipping primary key definitions entirely can hinder optimization. A composite key in 'SALES' is unnecessary as 'TRANSACTION_ID is already globally unique.

NEW QUESTION # 127
A company receives daily CSV files containing customer order data'. Each file contains a header row and is compressed using GZIP.
The files are landed in an AWS S3 bucket. The company wants to automate the data ingestion into a Snowflake table named 'orders table'. The requirements are: 1. Automated ingestion: New files should be automatically ingested as they arrive in the S3 bucket. 2. Data validation: Records with invalid dates or missing product IDs should be rejected and logged for review. 3. Data transformation: The column (string format 'YYYY-MM-DD') needs to be converted to a DATE data type, and a new column 'order _ year' needs to be derived from the 'order_date'. Which combination of Snowflake features and configurations provides the MOST efficient and reliable solution to meet these requirements?

A. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that includes 'ON ERROR = 'SKIP_FILE". Use a downstream task to periodically validate and transform the data in the 'orders_table'.
B. Create an external table pointing to the S3 bucket. Use a stream on the external table to track changes and a task to periodically move the new data into the 'orders_table' while performing the necessary transformations and validations. This could also be achieved using Dynamic Tables.
C. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that utilizes a user-defined function (UDF) written in Python to perform complex data validation and transformation before loading the data into the 'orders_table'. Set 'ON_ERROR = 'SKIP_FILEP to avoid loading erroneous data.
D. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that performs the date conversion using TO DATE() and extracts the order year using YEAR(). Configure the COPY INTO statement with "ON_ERROR = 'CONTINUE" and a validation table to log rejected records.
E. Create a Snowpipe that points to the S3 bucket. Use a COPY INTO statement with 'VALIDATE(O)' and a BEFORE trigger to invoke a stored procedure that validates the data against a set of rules. Use a stored procedure to transform the data into 'orders_table' .

Answer: B,D

Explanation:
Options B and C offer the best combination of features to address the requirements effectively. Option B leverages Snowpipe's COPY INTO statement to directly convert the date, calculate order year, and handle errors by continuing the load and logging invalid records into a validation table. This maximizes efficiency and ensures that valid data is ingested quickly. ON ERROR = 'CONTINUE' is better than SKIP FILE since it is preferable to ingest valid data in file even some has issues. Option C uses external tables combined with streams and tasks or dynamic tables which is an alternative to Snowpipe and COPY INTO and also provides automatic ingestion and transformation capabilities. Option A is less effective because it does not provide a mechanism to capture and log errors from the copy process; skipping files provides no insight to the validity of data. Option D is not the most efficient. While UDFs can handle complex transformations, relying solely on them for all validation and transformation steps can lead to performance bottlenecks and introduce maintenance overhead; Also setting ON_ERROR = 'SKIP_FILE' isn't a great pattern if you want to ingest partial data. Option E's BEFORE trigger might add significant overhead since Snowflake triggers have limitations.

NEW QUESTION # 128
You are building a Snowsight dashboard to monitor the performance of various SQL queries. You have a table 'QUERY HISTORY with columns 'QUERY ID', 'START TIME, 'END TIME, 'USER NAME, 'DATABASE NAME, and 'EXECUTION_TIME (in seconds). You want to create a bar chart that shows the average execution time for each user, but only for queries executed against a specific database (e.g., 'SALES DB') within the last week. Furthermore, you need to allow users to filter the data by username via a Snowsight dashboard variable. What is the most efficient SQL query and Snowsight configuration to achieve this?

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

Answer: D

Explanation:
Option B provides the most efficient solution by using a Snowsight dashboard variable ('USERNAME) with a dropdown populated from the distinct usernames in the 'QUERY HISTORY table. This allows users to easily filter the data by selecting a username from the dropdown, and the SQL query directly incorporates the variable using '$USERNAME in the WHERE clause for optimal performance. Option A lacks the dynamic filtering capability. Option C will not work as 'HAVING' clause should use aggregate functions, and even if it worked is not efficient. Option D would be difficult to maintain and less performant. Option E introduces unnecessary complexity with a stored procedure for this specific requirement.

NEW QUESTION # 129
......

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