Most Recent Snowflake DAA-C01 Exam Questions–Verified By Snowflake Experts

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. Window functions 2. PIVOT/UNPIVOT operations 3. Common Table Expressions (CTEs) 4. QUALIFY clauses - Design data models 1. Star schema design 2. Data vault models 3. Snowflake schema design
Topic 2: 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
Topic 3: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Snowflake-specific analytical features 3. Aggregate functions
Topic 4: Data Ingestion and Data Preparation	15%-20%	- Perform data discovery to identify what is needed from available datasets 1. Query tables to assess data elements and statistics maintained by Snowflake 2. Determine the level of data granularity required 3. Evaluate required transformations (table joins, set operations, ASOF JOINS) 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 5. Identify elements required for business goals using BI reports or SQL analysis - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - 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. Define primary keys for tables 2. Perform table joins between parent/child tables 3. Implement constraints - 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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which action is crucial in performing diagnostic analysis to identify reasons/causes of anomalies in historical data?

A. Ignoring demographic variations in the data
B. Collecting unrelated data and ignoring statistical trends
C. Analyzing data from isolated points in time
D. Collecting related data and demographics

Answer: D

Explanation:
Collecting related data and demographics is crucial in identifying reasons/causes of anomalies in historical data.

NEW QUESTION # 12
You are tasked with cleaning a 'customer_orders' table in Snowflake. The table contains columns like 'order_id', 'customer_id', 'order_date', and 'order_amount'. You notice that some 'order_amount' values are negative (representing returns), but you need to analyze total sales. Additionally, some 'order_date' values are in the future. Which of the following SQL transformations would BEST address these data quality issues to ensure accurate sales analysis?

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

Answer: A

Explanation:
Option C correctly addresses both issues. ensures that all amounts are positive, effectively treating returns as positive contributions to sales for the purpose of this specific analysis. 'order_date <= filters out future dates. Options A and D also exclude negative order amounts which is incorrect, whereas options B and E do not make all amounts positive for sales amount. The use of CTE is also correct. The question is testing the candidate on how to select the best transformations for given data challenges.

NEW QUESTION # 13
You are using a Snowflake Marketplace data feed that provides daily stock prices. The data is updated daily, and you need to create a process to automatically load the new data into your existing 'STOCK PRICES' table. The Marketplace data feed provides a view called 'MARKETPLACE STOCK PRICES' with columns 'DATE' (DATE), 'SYMBOL' (VARCHAR), and 'PRICE (NUMBER). Your 'STOCK PRICES' table has the same columns. Which of the following Snowflake features or techniques would be BEST suited for automatically loading the new data each day, ensuring that duplicate entries for the same 'DATE and 'SYMBOL' are avoided?

A. Create a Stream on the 'MARKETPLACE STOCK PRICES view, and then create a Snowflake Task that runs daily and processes the stream, inserting only new or changed data into the table. Schedule the Stream refresh frequency to once every day to match the new data feed frequency.
B. Create a Snowflake Task that runs daily and executes a simple 'INSERT INTO STOCK_PRICES SELECT FROM statement.
C. Create a Snowflake Task that runs daily and executes a 'MERGE statement to insert new data from into 'STOCK PRICES' , updating existing records if a match exists based on and 'SYMBOLS and inserting new records if there is no match. Also add a condition to remove the duplicate entries for the same 'DATE and ' SYMBOL'
D. Use Snowpipe Auto-Ingest with an external stage pointing to the Snowflake Marketplace data feed. Configure the pipe to copy new files from the Marketplace's internal stage to your table.
E. Create a Snowflake Pipe that automatically ingests data from the view into the 'STOCK_PRICES table whenever new data is available.

Answer: C

Explanation:
Option C is the most appropriate. A 'MERGE statement allows for both inserting new records and updating existing records based on a matching condition (in this case, and 'SYMBOL). This ensures that new data is added, and existing data can be updated if necessary. It also allows for handling potential duplicate scenarios. Option A will simply insert all data, leading to duplicates. Option B is not applicable as Snowpipe ingests from stages not views. Option D is incorrect as Snowpipe is designed for external stages, not internal Marketplace feeds. Option E, while a valid approach for change data capture, is more complex and unnecessary for a simple daily load, also the marketplace feeds don't support change data capture through Streams

NEW QUESTION # 14
A data analyst is using a data feed from the Snowflake Marketplace which delivers data in JSON format. They need to create a relational table to store this data, but the JSON structure is deeply nested and contains arrays. Which of the following approaches would be the MOST efficient way to create the table and load the JSON data, taking into consideration performance and minimizing data duplication?
(Choose two)

A. Use a stored procedure to parse the JSON data and insert it into a pre-defined relational table.
B. Create multiple relational tables, flattening the JSON structure using LATERAL FLATTEN and storing related data in separate tables.
C. Load the JSON data into a staging table, then use a series of INSERT statements with JSON parsing functions to populate the final relational table.
D. Create a view on top of the JSON data stored in a single VARIANT column, using JSON path expressions to extract specific fields.
E. Create a single table with a VARIANT column to store the entire JSON document.

Answer: B,C

Explanation:
Options B and E are the most efficient. Option B: Using 'LATERAL FLATTEN' allows you to break down the JSON structure into relational tables, minimizing data duplication and improving query performance for specific fields. Option E: Loading the JSON data into a staging table and then using INSERT statements with JSON parsing functions provides a controlled and efficient way to transform and load the data into the final relational table. Option A: While storing the entire JSON in a VARIANT column is easy, it can lead to performance issues for queries that need to access specific fields within the JSON. Option C: Stored procedures can be complex and less efficient than using native Snowflake JSON parsing functions. Option D: Creating views on VARIANT columns can still suffer from performance issues compared to flattened relational tables.

NEW QUESTION # 15
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

A. They improve data visibility for all users without restrictions.
B. Dynamic Data Masking doesn't affect data visibility in dashboards.
C. Both policies restrict data visibility for better security.
D. Row access policies limit data visibility based on user privileges.

Answer: D

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.

NEW QUESTION # 16
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