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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting
Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting
Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
When connecting BI tools to Snowflake for dashboard creation, what factors need to be considered for seamless integration? (Select all that apply)

A. Network latency between BI tool and Snowflake
B. Availability of a Snowflake account only
C. Data encryption requirements
D. Compatibility of BI tool with Snowflake

Answer: A,C,D

Explanation:
Seamless integration requires considering compatibility, encryption, and network latency between BI tools and Snowflake.

NEW QUESTION # 42
A financial institution uses Snowflake to store customer transaction data'. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the 'TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?

A. Create a temporary table that stores daily transaction summaries. Truncate and reload the temporary table hourly using a scheduled Snowflake task.
B. Implement caching within the dashboard application to store the query results and only refresh the data once a day to avoid performance issues
C. Increase the warehouse size to X-Large and rely on Snowflake's query optimization engine without any changes to the data model or update process.
D. Create a regular view that calculates daily transaction volume and average transaction amount. Use a Snowflake stored procedure to execute the query and update a separate reporting table hourly.
E. Create a materialized view that pre-aggregates the daily transaction volume and average transaction amount. Schedule a Snowflake task to refresh the materialized view hourly.

Answer: E

Explanation:
Materialized views are designed for pre-computation of aggregations, providing significant performance improvements. Scheduling a task to refresh the materialized view ensures automatic updates. Regular views are calculated at query time and would not improve performance. Increasing warehouse size (C) might help, but it's not the most efficient solution. Temporary tables are not persistent and truncation/reload is inefficient. Dashboard caching (E) does not solve underlying Snowflake performance issues.

NEW QUESTION # 43
You are tasked with aggregating website clickstream data in Snowflake to identify the most popular product categories per region on a daily basis. The clickstream data is stored in a table named 'clickstream eventS with columns: 'event_time', 'user id', 'product id', 'region', and 'category'. You need to create a solution that efficiently identifies the top 3 categories for each region on each day. Which approach offers the best performance and scalability considering the dataset size is expected to grow significantly?

A. Using a combination of GROUP BY, RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3.
B. Using a simple GROUP BY operation to count the occurrences of each category, region, and day, then relying on external tools to filter the top 3.
C. Using a combination of GROUP BY, DENSE_RANK() window function, and a subsequent filter. This approach calculates the rank for each category within each region and day, then filters to keep only the top 3. DENSE RANK handles ties more gracefully.
D. Implementing a stored procedure that iterates through each region and day, calculating the category counts and selecting the top 3 using procedural logic.
E. Creating a series of temporary tables for each region, aggregating the data, and then using a JOIN operation to combine the results.

Answer: C

Explanation:
Option D is the most efficient and scalable because it leverages Snowflake's built-in window functions (DENSE_RANK) for efficient ranking within partitions (region and day). Window functions are optimized for parallel processing. DENSE RANK handles ties appropriately (assigning the same rank to tied categories). Options A is similar but DENSE_RANK is better for the case of ties. Option B would be slow and not scalable due to the iterative nature of stored procedures. Option C is inefficient due to the creation of temporary tables and JOIN operations. Option E offloads the crucial filtering to external tools, impacting performance.

NEW QUESTION # 44
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?

Answer: A

Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.

NEW QUESTION # 45
You're developing a data quality process in Snowflake that relies on identifying duplicate records within a large table named 'TRANSACTIONS. You need to generate a hash value for each row based on several key columns ('transaction_id', 'customer_id' , amount', to efficiently compare rows and detect duplicates. However, some of these columns may contain NULL values, which you want to handle consistently during the hash generation. Which of the following approaches, utilizing Snowflake's system functions, will MOST reliably generate a consistent hash value for duplicate rows, even when some of the key columns contain NULLs? (Select TWO)

A. Use the customer_id, amount, transaction_date)' function directly, as Snowflake automatically handles NULLs in hashing functions.
B. Use the WS('I', transaction_id, customer_id, amount, function. This concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies.
C. Use the AS VARCHAR), AS VARCHAR), AS VARCHAR), NVL(CAST(transaction_date AS VARCHAR), function, explicitly converting each value to a string and replacing NULLs with empty strings using NVL.
D. Use the 'SHA2(CONCAT(NVL(transaction_id, 'I), NVL(customer_id, "), NVL(amount, "), NVL(transaction_date, "))) function, replacing NULLs with empty strings using the NVL function before concatenation.
E. Use the II customer_id II amount II transaction_date)' function. Snowflake implicitly converts NULL to a default value during string concatenation.

Answer: C,D

Explanation:
Options B and E are the most reliable. Option B concatenates the value of the columns as a string to create a seed for SHA2, ensuring to convert the NULL to empty string, which is necessary so that SHA2 does not return NULL in the face of NULL column values. Option E also uses SHA2 to encrypt after concatenating all the column values but it casts all those columns to varchar, which is necessary for the data preparation and data ingestion as they might be of different datatype. The first option is wrong because Snowflake's HASH function automatically returns NULL if any of the input are NULL. Option C uses the 'II' operator to concatenate values and Snowflake will return NULL in case any value is null. Option D concatenates strings with a separator, handling NULLs implicitly by skipping them in concatenation, leading to inconsistencies

NEW QUESTION # 46
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