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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion and Data Preparation	15%-20%	- Implement data processing solutions 1. Respond to processing failures 2. Automate and implement data pipelines (scheduling) 3. Cleanse, conform, and enrich data 4. Use logging and monitoring solutions (auditing, data lineage) - Perform data discovery to identify what is needed from available datasets 1. Evaluate required transformations (table joins, set operations, ASOF JOINS) 2. Identify elements required for business goals using BI reports or SQL analysis 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. Determine the level of data granularity required - 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. Retrieve data from unstructured sources 4. Synthetic Data Generation - 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 - Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Define primary keys for tables 3. Perform table joins between parent/child tables - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight
Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. QUALIFY clauses 2. Common Table Expressions (CTEs) 3. Window functions 4. PIVOT/UNPIVOT operations - Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design
Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Other partner visualization tools 2. Tableau integration 3. Power BI integration - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?

A. They enhance data visibility without any restrictions.
B. Both policies restrict data visibility for better security.
C. Row access policies limit data visibility based on user privileges.
D. Dynamic Data Masking doesn't impact dashboard creation or maintenance.

Answer: C

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.

NEW QUESTION # 13
You have a Snowflake table 'customer data' containing personally identifiable information (PII) such as customer names and addresses. You need to create a de-identified version of this table for analytical purposes, replacing the names and addresses with synthetic data, while preserving referential integrity with other tables that use the original customer IDs. Which combination of Snowflake features and techniques would you use to achieve this securely and efficiently?

A. Export the 'customer_data' table to a local file, de-identify the data using an external scripting language (e.g., Python), and then import the de-identified data back into Snowflake.
B. Create a view on top of the original table using Snowflake's masking policies and row-level security, allowing only specific users to see the de-identified data.
C. Use Snowflake's data masking policies to dynamically mask the PII columns during query execution.
D. Use Snowflake's external functions to call an external service that provides de-identification capabilities.
E. Create a new table with the de-identified data using a combination of Snowflake's HASH function for customer IDs, and a user-defined function (UDF) to generate synthetic names and addresses. Use secure data sharing to provide access to the de-identified table.

Answer: D,E

Explanation:
Options B and D are the best choices. Option B directly addresses the need for a de-identified table by generating synthetic data and using HASH to preserve referential integrity. Secure data sharing provides controlled access. Option D uses external functions to leverage specialized de-identification services. Options A and E are useful for dynamic masking, but do not create a separate de-identified dataset and may still expose PII to authorized users. Option C involves exporting data, which increases the risk of data leakage and is less efficient.

NEW QUESTION # 14
A data analyst is implementing a data preparation pipeline using Snowflake stored procedures to cleanse and transform data,. During testing, the analyst encounters unexpected errors within the stored procedures. Which strategies should the analyst employ to effectively debug and troubleshoot these stored procedures within Snowflake?

A. Use the 'GET function on the stored procedure to review the code for potential errors.
B. Rely solely on the error messages returned by Snowflake when the stored procedure fails.
C.
D. Implement a custom error logging mechanism within the stored procedure using 'SYSTEM$LOG' to capture error messages and write them to a dedicated logging table.
E. Debug by trial and error, modifying the stored procedure code and re-executing until the error is resolved.

Answer: C,D

Explanation:
Option A describes the correct method for obtaining the query ID of the failing procedure and querying the query history for detailed error information. Option B is also correct, implementing detailed error handling within the stored procedure provides greater insight into where the procedure fails. Option C is insufficient, Snowflake's general error messages are not always descriptive. Option D does not help during debugging and troubleshooting a running stored procedure. Option E is an inefficient and unreliable debugging approach.

NEW QUESTION # 15
You are tasked with improving the performance of queries against a very large table named 'SALES DATA, which contains sales transactions from multiple regions. The table is frequently queried based on 'REGION' , 'SALE DATE, and 'PRODUCT CATEGORY. The data in 'SALE DATE' ranges from 2020 to the present. You want to optimize query performance by leveraging Snowflake's clustering capabilities, but you also want to minimize the impact of data ingestion and maintenance. Considering the characteristics of the data and query patterns, which of the following strategies would be the most effective and cost-efficient approach for clustering 'SALES DATA'?

A. Cluster by ' REGION' and only. Snowflake will automatically optimize for 'PRODUCT_CATEGORY due to its internal statistics and query optimization.
B. Create a materialized view pre-aggregating the data by 'REGION' , 'SALE_DATE, and 'PRODUCT_CATEGORY'. No explicit clustering is required on the base table.
C. Partition the table by 'SALE_DATE and cluster within each partition by ' REGIO!V and PRODUCT_CATEGORY.
D. Cluster by 'SALE DATE only. Snowflake's automatic clustering will handle the other dimensions over time, and this minimizes upfront clustering costs.
E. Cluster by 'REGION', 'SALE DATE , and 'PRODUCT CATEGORY in that order. Monitor clustering depth frequently and rebuild the table if clustering depth exceeds a threshold.

Answer: E

Explanation:
Option A, clustering by 'REGION', 'SALE DATE', and 'PRODUCT CATEGORY' in that order is the most effective approach. Given the frequent filtering on these three columns, clustering on them ensures that Snowflake can efficiently prune the data and minimize the amount of data scanned during query execution. By monitoring clustering depth and rebuilding when it exceeds a threshold, you can maintain optimal performance over time as new data is ingested. Option B is not ideal because SALE_DATE alone might not be sufficient for all query patterns. Option C is not ideal because Snowflake does not automatically optimize 'PRODUCT_CATEGORY only because of stats. Option D, Materialized view might not be good because they are usually pre aggregated and in this scenario are required raw data. E is invalid because partition does not exist in snowflake.

NEW QUESTION # 16
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?

A. They restrict access to external data.
B. Data shares only work with Snowflake-provided datasets.
C. Data shares facilitate secure data exchange between parties.
D. They limit the types of data that can be joined.

Answer: C

Explanation:
Data shares enable secure data exchange, allowing joining external data with existing datasets.

NEW QUESTION # 17
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