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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Tableau integration
  • 2. Power BI integration
  • 3. Other partner visualization tools
- Create dashboards
  • 1. Present analytical results
  • 2. Snowsight dashboards
Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Window functions
  • 3. Common Table Expressions (CTEs)
  • 4. QUALIFY clauses
- Design data models
  • 1. Snowflake schema design
  • 2. Star schema design
  • 3. Data vault models
Data Ingestion and Data Preparation15%-20%- Use a collection system to retrieve data
  • 1. Synthetic Data Generation
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from structured sources (CSV)
  • 4. Retrieve data from unstructured sources
- 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
- 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 data from external/internal stages into a table
  • 2. Load files using Snowsight
- Implement data processing solutions
  • 1. Automate and implement data pipelines (scheduling)
  • 2. Respond to processing failures
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Cleanse, conform, and enrich data
- Perform data discovery to identify what is needed from available datasets
  • 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 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. Determine the level of data granularity required
Data Analysis30%-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 (Q59-Q64):

NEW QUESTION # 59
A large, complicated query is used to generate a data set for a report on the most recent month. It is taking longer than expected. A review of the Query Profile shows excessive spilling. How can the performance of the query be improved WITHOUT increasing costs?

Answer: B

Explanation:
In Snowflake, "spilling" occurs when the intermediate data required to process a query exceeds the available memory (RAM) of the virtual warehouse. When this happens, Snowflake "spills" the data first to the warehouse's local SSD and, if that fills up, to remote storage (e.g., S3 or Azure Blob). This significantly degrades performance due to increased I/O latency.
To resolve this without increasing costs (i.e., without scaling up to a larger warehouse size), an analyst must reduce the memory footprint of the query.
* CTEs vs. Temporary Tables: While Common Table Expressions (CTEs) are excellent for readability, they are often treated as logical entities that the optimizer may re-evaluate multiple times or struggle to keep in memory during massive joins.
* Batch Processing: By splitting a single monolithic query into multiple steps and storing intermediate results in temporary tables, the analyst forces the engine to clear its memory between steps. This process acts as a "checkpoint," effectively processing the data in smaller, more manageable batches that fit within the current warehouse's memory limits.
Evaluating the Options:
* Option A is incorrect because spilling is a compute-resource issue, not a storage-lock or contention issue.
* Option B is incorrect because materialized views and clustering incur background credit costs for maintenance, violating the "without increasing costs" constraint.
* Option C is incorrect because external tables are generally slower than native Snowflake tables due to the lack of proprietary metadata and micro-partitioning.


NEW QUESTION # 60
You are tasked with enriching your company's customer transaction data with external economic indicators (e.g., unemployment rate, GDP) obtained from a Snowflake Marketplace data provider. The transaction data resides in a table 'TRANSACTIONS' with columns 'TRANSACTION (INT), 'TRANSACTION DATE (DATE), and (VARCHAR). The economic indicators data, obtained from the Marketplace, is available in a table 'ECONOMIC DATA' with columns 'DATE (DATE), ZIP_CODE (VARCHAR), 'UNEMPLOYMENT RATE (NUMBER), and 'GDP' (NUMBER). Due to data quality issues, some zip codes in both tables are missing or malformed. You need to create a view that efficiently joins these two tables, handles missing or malformed zip codes, and provides the transaction data enriched with the economic indicators. Which of the following approaches is the MOST robust and efficient way to create this enriched view, minimizing data loss and maximizing data quality?

Answer: B

Explanation:
Option C provides the most robust and efficient solution. Using 'LEFT OUTER JOIN' ensures that all transactions are included in the view, even if there is no matching economic data. 'TRY TO NUMBER handles malformed zip codes gracefully by converting valid zip codes to numbers and returning NULL for invalid ones, preventing errors. 'NVL' replaces NULL zip codes (either originally missing or resulting from TRY_TO_NUMBER) with a default value, allowing the join to proceed using a fallback. Adding the 'ENRICHMENT_SUCCESS' flag provides transparency about which records were enriched using the default zip code, enabling users to assess the reliability of the enriched data. Option A is inadequate because it ignores missing or malformed zip codes, leading to data loss. Option B is inefficient and not scalable due to row-by-row processing. Option D discards records with missing or malformed zip codes, resulting in significant data loss. Option E does not specifically handle data quality issues related to missing or malformed zip codes. Further the use of Tasks and materialized views, while increasing performance, doesn't necessarily address the issue of data quality.


NEW QUESTION # 61
Which Snowflake feature or object can be used to dynamically create and execute SQL statements?

Answer: B

Explanation:
While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.
Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.


NEW QUESTION # 62
A marketing team wants to understand the impact of their campaigns on website traffic and conversions. You have the following tables in Snowflake: sCAMPAlGN PERFORMANCE: 'CAMPAIGN ONT), (DATE), 'CLICKS' (INT), 'IMPRESSIONS' (INT), 'COST (NUMBER) 'DATE' (DATE), 'PAGE_VIEWS' ONT), (INT) 'CONVERSIONS' 'DATE (DATE), ONT), (NUMBER) Which SQL query and visualization technique would be most suitable for identifying the correlation between campaign spend and website conversions over time, allowing the team to quickly identify campaigns with a high return on investment (ROI)?

Answer: C

Explanation:
Option D is the most effective. A line chart plotting cost, unique visitors, and conversion count over time allows for visual identification of trends and correlations. Displaying ROI on the same chart provides a direct measure of campaign effectiveness. The CTE helps organize the ROI calculation. Option B, while useful, doesn't explicitly show the temporal relationship. Options A and C offer limited insights into ROI. Exporting and using Excel (E) is not scalable or efficient for dynamic analysis.


NEW QUESTION # 63
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.

Answer: D

Explanation:
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.


NEW QUESTION # 64
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