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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Snowsight dashboards 2. Present analytical results
Topic 2: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Time-series analysis 3. Snowflake-specific analytical features
Topic 3: Data Transformation and Data Modeling	22%-30%	- Design data models 1. Star schema design 2. Data vault models 3. Snowflake schema design - Transform data using SQL 1. Window functions 2. PIVOT/UNPIVOT operations 3. Common Table Expressions (CTEs) 4. QUALIFY clauses
Topic 4: Data Ingestion and Data Preparation	15%-20%	- Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Implement constraints 3. Define primary keys for tables - 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. Evaluate required transformations (table joins, set operations, ASOF JOINS) 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Identify elements required for business goals using BI reports or SQL analysis 5. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) - 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) - 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 semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Retrieve data from unstructured sources

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
A data analyst is tasked with identifying the top 3 performing sales representatives in each region based on their total sales amount. The sales data is stored in a table named 'sales data" with columns 'region', 'sales_rep', and 'sales_amount'. Which Snowflake SQL statement(s) would efficiently achieve this?

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

Answer: B,D,E

Explanation:
Options A, B, and C correctly utilize window functions to rank sales representatives within each region. assigns a unique rank, assigns the same rank to ties and skips the subsequent ranks, and 'DENSE RANK()' assigns the same rank to ties but does not skip ranks. All three can be used with a 'WHERE clause to filter for the top 3. Option D incorrectly uses 'LAG' and doesn't achieve the desired ranking. Option E uses 'NTILE' which divides the rows into three groups; filtering 'tile = 1' might not always return the top three based on sales.

NEW QUESTION # 15
You are tasked with building a dashboard in Looker Studio to visualize data from a Snowflake database. The data contains sensitive information, and the security team requires that only authorized users can access specific data based on their role. The Snowflake database has roles defined: 'ANALYST, 'MANAGER, and 'EXECUTIVE. The 'SALES DATA' table contains a 'REGION' column. 'ANALYST' should only see data for their assigned region, 'MANAGER should see data for their region and direct reports regions, and 'EXECUTIVE should see all regions. Which of the following is/are the MOST secure and efficient way(s) to implement row-level security in Snowflake and integrate it with Looker Studio without duplicating the data?

A. Create separate Snowflake views for each role, filtering the data based on the 'REGION' column. Connect Looker Studio to the appropriate view based on the user's role.
B. Implement a Snowflake stored procedure that accepts a user's role as input and returns the filtered data. Connect Looker Studio to this stored procedure.
C. Implement Row Access Policies in Snowflake based on the USER NAME() or CURRENT ROLE() functions to filter the 'SALES DATA' table directly. Grant appropriate roles to users and connect Looker Studio using a service account that has the necessary privileges.
D. Import the 'SALES DATA' table into Looker Studio and implement data blending with a user role mapping table to filter the data within Looker Studio.
E. Create dynamic data masking policies in Snowflake to redact sensitive 'SALE_AMOUNT data based on roles and connect Looker Studio using a service account.

Answer: C,E

Explanation:
Row Access Policies (RAP) in Snowflake provide the most secure and efficient way to implement row-level security. By defining policies based on the user's role, you can ensure that users only see the data they are authorized to access directly at the Snowflake level. Combining RAPs with dynamic data masking for 'SALE_AMOUNT adds an extra layer of security by redacting sensitive data based on the user's role. Option A requires managing multiple views, which can be cumbersome. Option B introduces complexity with stored procedures. Option D moves security logic into Looker Studio, which is less secure and can be bypassed. Using a service account with appropriate privileges ensures that Looker Studio can access the data securely and apply the defined Row Access Policies. Options C is correct because it keeps security logic within Snowflake. Option E is correct because it provides additional data masking depending on the security policies for sale amount.

NEW QUESTION # 16
What is the primary purpose of implementing data processing solutions?

A. Limit data processing to batch operations
B. Automate data analysis entirely
C. Simplify data access for users
D. Respond effectively to processing failures

Answer: D

Explanation:
Data processing solutions help respond effectively to processing failures for uninterrupted operations.

NEW QUESTION # 17
You are working with a Snowflake environment where data analysts need to query Parquet files located in AWS S3. The analysts require the ability to perform complex aggregations and joins with other Snowflake tables. Some of the Parquet files have evolved their schema over time, adding new columns. Given these requirements, which set of actions would provide the MOST efficient and maintainable solution? Choose the THREE most appropriate actions.

A. Develop custom SQL scripts to manually extract data from the external table into temporary tables with specific schemas, then perform aggregations and joins using those temporary tables.
B. Implement a process to periodically load the Parquet files into Snowflake internal tables using 'COPY INTO , inferring the schema from the Parquet files and creatin a new table version whenever the schema changes. Create views to handle the multi le table versions.
C. Implement a data pipeline that continuously ingests the Parquet data into a Snowflake internal table with a defined schema, ensuring all possible columns are included and using a VARIANT type for potentially evolving fields.
D. Create materialized views on top of the external table, extracting frequently used columns and performing common aggregations to pre-compute results.
E. Create an external table with 'AUTO REFRESH = TRUE' and use schema evolution features to automatically detect and accommodate new columns in the Parquet files. use parameter to tune query performance.
F. Create an external table in Snowflake pointing to the S3 bucket and use the 'VALIDATE(100);' command frequently to validate if the stage data integrity

Answer: D,E,F

Explanation:
'AUTO REFRESH = TRUE' handles schema evolution in external tables. Materialized views on the top of external tables provides precomputed data, which is good for aggregations. The VALIDATE command allows analysts to check the stage data integrity and avoid errors in the data pipeline. Ingesting all data into a single VARIANT column is not efficient for aggregations and joins. Extracting to temporary tables is not maintainable. Periodically creating new tables is complex to manage. Adjusting can help improve performance.

NEW QUESTION # 18
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

A. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.
B. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.
C. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
D. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.
E. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.

Answer: E

Explanation:
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

NEW QUESTION # 19
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