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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
When connecting BI tools to Snowflake for dashboard creation, what factors need to be considered for seamless integration? (Select all that apply)

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

Answer: B,C,D

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

NEW QUESTION # 64
You have a table 'ORDERS that stores customer order information. Due to a faulty ETL process, incorrect discount values were applied to some orders yesterday. You need to correct these discount values while ensuring that you can revert to the original data if necessary. Select all the valid actions to mitigate this situation using Snowflake's Time Travel and Cloning features.

A. Update the discount values directly in the 'ORDERS' table after verifying the incorrect records, relying solely on Snowflake's data retention for recovery.
B. Create a clone of the 'ORDERS' table BEFORE applying the corrections, allowing you to revert to the original data by cloning the backup back if needed.
C. Create a temporary table to store the original incorrect discount values before applying the corrections, then updating the 'ORDERS' table.
D. Use Time Travel to query the 'ORDERS' table as it existed BEFORE the faulty ETL process, then use this data to correct the discount values in a separate update statement.
E. Create a backup table with the structure and all the records by using 'create table orders_backup as select from orders'.

Answer: B,D

Explanation:
Options A and E are the most reliable and efficient. Cloning the table (A) before the correction provides a complete, point-in-time backup that can be easily restored. Using Time Travel (E) allows you to query the historical data to identify the correct values and apply targeted updates. B is risky without a backup, as unforeseen issues might prevent successful recovery within the retention period. While C is technically feasible, it is less robust and adds complexity compared to a full clone or Time Travel. D is same as C but with more data, but less robust and adds complexity compared to a full clone or Time Travel. Creating the temp table doesn't give you the benefit of time travel, so doesn't make sense.

NEW QUESTION # 65
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?

Answer: B

Explanation:
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.

NEW QUESTION # 66
A data analyst observes a sudden and significant drop in sales for a particular product category within a Snowflake database. Initial investigations point to a possible data quality issue. Which of the following steps provides the MOST effective and efficient diagnostic approach using Snowflake features to pinpoint the root cause of the anomaly, focusing on data integrity?

A. Analyze query history in Snowflake to identify any recent DML (Data Manipulation Language) operations (e.g., UPDATE, DELETE) performed on the sales table, focusing on operations targeting the affected product category. Correlate these operations with the timeframe of the sales drop.
B. Utilize Snowflake's Time Travel feature to compare the data before and after the suspected anomaly. Query both datasets and perform set operations like EXCEPT or MINUS to identify changed records within the product category. Analyze those changed records for patterns.
C. Rebuild the entire product sales table from the raw data source to ensure data consistency, as the drop in sales suggests widespread data corruption. Compare the rebuilt table with the current table.
D. Disable all ETL processes and re-ingest the sales data overnight. This will overwrite the potential problematic sales data and resolve the drop in sales.
E. Create a clone of the sales table. Run a full table scan on the clone and perform descriptive statistics on the cloned data to detect statistical outliers without impacting the production environment. Compare with known good statistics.

Answer: A,B

Explanation:
Options A and C are the most effective. A utilizes Time Travel for direct data comparison before and after the incident, allowing for focused analysis on changed records. C investigates DML operations, which could directly explain data changes. Option B is inefficient and disruptive. Option D, while helpful, might not pinpoint the cause of a data corruption issue as fast as A and C. Option E is a poor solution, as it doesn't identify the root cause of the issue and it leads to potential data lost from the transactions between the last successful load and when the ETL processes were disabled.

NEW QUESTION # 67
You're analyzing a sales dataset in Snowflake. The dataset contains 'customer id', 'product id', 'sale date', and 'sale amount'. You've noticed that some 'sale_amount' values are negative, which is illogical. You also suspect duplicate records, indicated by identical 'customer_id', 'product_id', and 'sale_date'. Additionally, a business rule dictates that sale amounts must be less than $10,000. Which combination of Snowflake SQL statements is the MOST efficient and accurate way to address these data quality issues?

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

Answer: A

Explanation:
Option E correctly handles all specified data quality issues. It replaces negative sale amounts with 0, enforces the business rule (sale_amount < 10000), and removes duplicate records based on 'customer_id', 'product_id', and 'sale_date' using the QUALIFY clause and ROW_NUMBER() window function, selecting only the first occurrence of each duplicate set. Options A and D do not handle negative sales amount correctly, Option B uses IFF(sale_amount < O, O, sale_amount) but do not handle boundary values of sale_amount < 10000, Option C considers sale amount between 0 and 10000.

NEW QUESTION # 68
......

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