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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Snowflake schema design 3. Star schema design - Transform data using SQL 1. PIVOT/UNPIVOT operations 2. QUALIFY clauses 3. Window functions 4. Common Table Expressions (CTEs)
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Time-series analysis 3. Snowflake-specific analytical features
Data Ingestion and Data Preparation	15%-20%	- Implement data processing solutions 1. Cleanse, conform, and enrich data 2. Use logging and monitoring solutions (auditing, data lineage) 3. Respond to processing failures 4. Automate and implement data pipelines (scheduling) - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Find external data sets that correlate with available data 2. Create tables and views 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Perform data discovery to identify what is needed from available datasets 1. Determine the level of data granularity required 2. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Evaluate required transformations (table joins, set operations, ASOF JOINS) 5. Identify elements required for business goals using BI reports or SQL analysis - Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Retrieve data from structured sources (CSV) 4. Synthetic Data Generation - Use best practice considerations relating to data integrity structures 1. Define primary keys for tables 2. Implement constraints 3. Perform table joins between parent/child tables
Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools - Create dashboards 1. Present analytical results 2. Snowsight dashboards

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?

A. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
B. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.warehouse_metering_history WHERE start_time >= DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
C. SELECT user_name, SUM(credits_used_compute) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
D. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.execution_history WHERE start_time >= DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
E. SELECT user_name, SUM(credits_used_cloud_services) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time >=DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;

Answer: C

Explanation:
The correct query should use the 'snowflake.account_usage.query_history' view and specifically sum the column as this reflects the credits used for the compute resources by query. Option C accurately reflects this. Option A is incorrect because the 'credits_used' column in 'query_history' does not give a direct credit consumption cost based on computation. Option B uses 'execution_history' which does not aggregate the same detailed credit usage information. Option D sums credits used for cloud services not the compute. Option E references warehouse metering history, not specific users' query execution history.

NEW QUESTION # 54
You have two Snowflake tables: 'transactions' (containing transaction details with columns 'transaction id', 'customer id', 'amount' , 'transaction_date') and 'customer_demographicS (containing customer demographic information with columns 'customer_id', 'age' , gender' , 'location'). You need to enrich the 'transactions' table with customer demographics to analyze transaction patterns based on customer segments. What is the most efficient and scalable way to achieve this data enrichment in Snowflake, considering the 'transactions' table contains billions of rows?

A. Using a series of correlated subqueries to fetch the demographic information for each transaction record.
B. Using Snowflake's materialized views to pre-compute the enriched data and refresh it periodically.
C. Using a standard INNER JOIN between the 'transactions' and 'customer_demographics' tables in a CREATE TABLE AS SELECT (CTAS) statement.
D. Export the tables to a data lake, perform the join using a Spark cluster, and then import the enriched data back into Snowflake.
E. Using Snowflake Streams and Tasks to incrementally enrich the 'transactions' table as new data arrives.

Answer: B,C,E

Explanation:
Options A, C, and D are efficient approaches. Option A (CTAS with INNER JOIN) is often the simplest and most performant for a one- time enrichment, leveraging Snowflake's query optimization. Option C (Materialized Views) provides pre-computed enriched data, ideal for frequent queries on the enriched dataset. Option D (Streams and Tasks) enables incremental enrichment, suitable for real-time or near real-time data updates. Option B (correlated subqueries) is generally inefficient and should be avoided with large datasets. Option E introduces unnecessary complexity by exporting data to a data lake.

NEW QUESTION # 55
You are analyzing website traffic data in Snowflake. The 'web_events' table contains 'event_timestamp' (TIMESTAMP N T Z), 'user_id', and 'page_url'. You discover that many 'event_timestamp' values are significantly skewed towards the future (e.g., a year ahead), likely due to incorrect device clocks. You want to correct these skewed timestamps by assuming the majority of events are valid and calculating a time drift. Which of the following strategies using Snowflake functionality would be MOST efficient and accurate for correcting these timestamps?

A. Calculate the median 'event_timestamp' for each 'user_id' and subtract the overall median 'event_timestamp' from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
B. Calculate the average 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
C. Calculate the median 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the median. Subtract this difference from the future skewed events to correct them.
D. Calculate the mode of the 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
E. Calculate the average 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the average. Subtract this difference from the future skewed events to correct them.

Answer: C

Explanation:
Option D provides the most robust approach. Using the median minimizes the impact of outliers (future-dated timestamps). Calculating the difference between each event timestamp and the overall median timestamp isolates the 'time_drift' for each record, which is then subtracted from each future skewed events. Option A uses median for each user, which is unneccesary. Options B and E are vulnerable to outliers (the very problem we're trying to solve). Option C, while conceptually interesting, isn't directly supported as a native aggregate function for timestamps in most SQL dialects, including Snowflake, without custom user-defined functions (UDFs), making it less efficient and potentially less accurate.

NEW QUESTION # 56
A Data Analyst has a Parquet file stored in an Amazon S3 staging area. Which query will copy the data from the staged Parquet file into separate columns in the target table?

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

Answer: D

Explanation:
In the Snowflake ecosystem, Parquet is treated as a semi-structured data format. When you stage a Parquet file, Snowflake does not automatically parse it into multiple columns like it might with a flat CSV file.
Instead, the entire content of a single row or record is loaded into a single VARIANT column, which is referenced in SQL using the positional notation $1.
The fundamental mistake often made-and represented in Option A-is treating Parquet as a delimited format where $1, $2, and $3 refer to different columns. In Parquet ingestion, columns $2 and beyond will return NULL because the schema is contained within the object in $1.
To successfully "shred" or flatten this semi-structured data into a relational table with separate columns, an analyst must use path notation. This involves referencing the root object ($1), followed by a colon (:), and then the specific element key (e.g., $1:o_custkey). Furthermore, because the values extracted from a Variant are technically still Variants, they must be explicitly cast to the correct data type using the double-colon syntax (e.g., ::number, ::date) to ensure they land in the target table with the correct data types.
Evaluating the Options:
* Option A is incorrect because it uses positional references ($2, $3, etc.) which are only valid for structured files like CSVs.
* Option B is incorrect because it attempts to reference keys directly without the required stage variable ($1) and colon separator.
* Option D is incorrect as it uses a non-standard parse() function that does not exist for this purpose in Snowflake SQL.
* Option C is the 100% correct syntax. It correctly identifies that the Parquet data resides in $1, utilizes the colon to access internal keys, and applies the necessary type casting. This specific method is known as "Transformation During Ingestion" and is a core competency for any SnowPro Advanced Data Analyst.

NEW QUESTION # 57
How does automating and implementing data processing contribute to the overall efficiency of data ingestion?

A. Automation eliminates human intervention, ensuring consistency.
B. Implementing data processing increases data redundancy.
C. It slows down the data ingestion process significantly.
D. Automating processing hampers data accuracy.

Answer: A

Explanation:
Automation ensures consistency and eliminates manual interventions, enhancing the efficiency of data ingestion.

NEW QUESTION # 58
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