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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are tasked with identifying potential fraud in a financial transactions table named 'transactions'. The table includes 'transaction id' , account id', 'transaction date', and 'transaction_amount'. You need to flag transactions where the transaction amount is significantly higher than the average transaction amount for that account within the last 7 days, specifically, three standard deviations higher. Which analytic function or combination of functions would best accomplish this, while optimizing for performance? (Select all that apply)

A. Utilize the ' NTILE()' window function to divide transactions into quantiles based on amount within each account's 7-day window. Flag transactions falling into the highest quantile as potentially fraudulent.
B. Calculate the average transaction amount for each account over the entire dataset. Flag transactions exceeding three times this global average.
C. Calculate the median transaction amount using the 'MEDIAN()' window function over a 7-day window. Flag transactions where the amount exceeds the median by a predefined percentage (e.g., 200%).
D. Use a user-defined function (UDF) to iterate through each account's transactions, calculate the average and standard deviation for the last 7 days, and flag transactions exceeding the threshold.
E. Calculate the average and standard deviation of transaction amounts for each account using 'AVG()' and window functions over a 7-day window, and then use a 'CASE' statement to flag transactions that exceed the threshold (average + 3 standard deviation).

Answer: E

Explanation:
Option A is the best approach for accurately identifying outliers using statistical measures. Calculating both the average ('AVG()') and standard deviation using window functions over a 7-day window allows for a dynamic threshold that adapts to each account's transaction behavior. Combining this with a 'CASE statement allows you to flag those transactions which are above three standard deviations from the average. Option B is less efficient than using Snowflake's built-in analytic functions. Options C, D, and E do not correctly identify outliers based on the specified criteria. The MEDIAN can hide many outliers, and the total dataset average and NTILE are less accurate.

NEW QUESTION # 17
What distinguishes exploratory ad-hoc analyses from routine data analysis?

A. Ad-hoc analyses rely solely on predefined queries.
B. Ad-hoc analyses focus only on established trends.
C. Ad-hoc analyses explore patterns and anomalies beyond established routines.
D. They query known patterns without further exploration.

Answer: C

Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.

NEW QUESTION # 18
A Data Analyst created two functions and one procedures:
The Analyst then runs this query:

The Analyst then runs this query:

What will be the output?

A. 0
B. 1
C. Null
D. 2

Answer: A

Explanation:
To determine the output of the final statement, a Data Analyst must perform a step-by-step trace of the code execution, following standard order of operations (inner-to-outer) and procedural logic within the Snowflake Scripting environment.
Step 1: Evaluating the innermost function, func2(10)
The definition for func2 takes a number p and returns p + 10.
* Input: 10
* Calculation: $10 + 10 = 20$
* Result: 20
Step 2: Evaluating the next function, func1(20)
The definition for func1 takes a number v and returns v + 10.
* Input: 20 (the result from Step 1)
* Calculation: $20 + 10 = 30$
* Result: 30
Step 3: Evaluating the procedure call, proc1(30)
The procedure proc1 takes an input variable named result. It then executes a conditional IF statement:
* Conditional Check: IF (result >= 30)
* Since the input is 30, the condition evaluates to True.
* Action: The procedure executes RETURN result - 10.
* Calculation: $30 - 10 = 20$
Summary of Logic:
The nested calls flow as follows: call proc1(30) $\rightarrow$ proc1 evaluates $30 \ge 30$ $\rightarrow$ proc1 returns $30 - 10$, which is 20. If the initial input had been lower, such that the final result passed to the procedure was less than 30, it would have followed the ELSE branch and returned the original value. This question evaluates the analyst's ability to interpret modular code and procedural control flow, which are key components of the Data Transformation and Data Modeling domain in the SnowPro Advanced: Data Analyst exam.

NEW QUESTION # 19
You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:

Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?

A. Add a 'ORDER BY DATE clause to the 'SALES SUMMARY' view definition.
B. Partition the 'SALES_TRANSACTIONS' table by the DATE column.
C. Create a materialized view on top of
D. Create a search optimization policy on the DATE column of the SALES_TRANSACTIONS table.
E. create a clustering key on the DATE column in the 'SALES_TRANSACTIONS' table.

Answer: E

Explanation:
Clustering the 'SALES_TRANSACTIONS table by the DATE column is the most effective solution. Snowflake uses micro- partitioning, and clustering helps to logically group similar data together within micro-partitions. When filtering by DATE, Snowflake can then efficiently prune micro-partitions that do not contain relevant data, significantly reducing the amount of data scanned. Materialized views (A) can help but need to be maintained and add complexity. Search optimization on DATE can help but it's not the intended use case, as DATE is a range based column (B). Ordering the results (C) doesn't affect query performance significantly. Partitioning is not directly supported in Snowflake. Clustering is the correct approach.

NEW QUESTION # 20
A financial institution stores transaction data with various attributes, including transaction ID, account ID, transaction type, amount, and timestamp. They need to build a dashboard to monitor transaction volumes and identify unusual patterns in real-time. The data is streaming continuously. Consider these requirements: near real-time analysis, complex pattern recognition, and the need to store the raw transaction data for auditing. Which of the following approaches is optimal, considering the need for both speed and historical data integrity?

A. Create a highly flattened table and use Snowflake's materialized views to aggregate data in near real-time. Store raw data in the same flattened table.
B. Directly query the raw streaming data using Snowflake's external tables, without any data transformation or modeling. Rely on Snowflake's query optimization for real-time analysis.
C. Create a single denormalized table, indexed on transaction ID and timestamp. Use this table for both real-time analysis and historical storage.
D. Use Snowflake Streams and Tasks to incrementally update a star schema data model in near real-time, with separate tables for transactions, accounts, and transaction types. Store raw data in a separate, immutable table.
E. Use Snowflake's Data Marketplace to access pre-aggregated financial data, avoiding the need for any data modeling or transformation.

Answer: D

Explanation:
Option B is the most suitable solution. Snowflake Streams and Tasks provide a mechanism for incrementally updating a data model (in this case, a star schema) based on changes in the source data. This enables near real-time updates to aggregated tables used for the dashboard. Storing the raw data in a separate, immutable table ensures data integrity for auditing purposes. Option A might work, but managing large flattened tables with constant updates can be challenging. Option C is not efficient for complex analysis. Option D lacks the structure needed for complex joins and aggregations. Option E is irrelevant as it proposes using external data instead of processing the institution's own data.

NEW QUESTION # 21
......

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