Valid DAA-C01 Test Registration - DAA-C01 Actual Exams

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
What critical role do user-defined functions (UDFs) play in SQL for advanced data analysis?

A. UDFs only handle basic data manipulation tasks.
B. UDFs are limited to specific data types.
C. They enable customized operations on data, extending SQL functionalities.
D. UDFs hinder query optimization in SQL.

Answer: C

Explanation:
UDFs extend SQL functionalities by enabling customized operations on data, going beyond standard SQL capabilities.

NEW QUESTION # 38
You have a Snowflake table named 'customer transactionS with columns: 'customer id', 'transaction date', 'transaction amount, and product_categorV. You need to identify customers who have made purchases in more than three different product categories within the last 30 days. Which of the following Snowflake SQL queries is the MOST efficient and accurate way to achieve this, considering the large size of the table?

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

Answer: D

Explanation:
Option C is the most efficient. It directly groups by 'customer_id' and uses the 'HAVING' clause to filter customers who have purchased from more than three distinct product categories within the specified date range. Option A is functionally correct but less concise. Option B uses an inefficient subquery. Option D returns all customer IDs who purchased from any product category within the timeframe. Option E attempts to use window functions and ARRAY AGG, which is unnecessarily complex and less performant for this task.

NEW QUESTION # 39
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

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

Answer: C

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

NEW QUESTION # 40
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?

A. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.
B. Create a materialized view and schedule a daily refresh at a time of low system activity.
C. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.
D. Create a materialized view and set the refresh schedule to 'ON CHANGE'.
E. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on

Answer: B

Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.

NEW QUESTION # 41
You have a table 'CUSTOMER LOCATIONS with customer IDs and their geographical locations in WGS 84 format stored in 'LATITUDE' (FLOAT) and 'LONGITUDE' (FLOAT) columns. You need to create a new table 'CUSTOMER GEOGRAPHY' with a 'LOCATION' column of GEOGRAPHY type derived from these latitude and longitude values. Which of the following statements, used individually or in combination, are necessary and correct to accomplish this?

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

Answer: D

Explanation:
Option D correctly creates the table using a 'CREATE TABLE AS SELECT (CTAS) statement and uses TO LATITUDE))' to convert the latitude and longitude values into a GEOGRAPHY object. This is a concise and efficient way to create the new table with the desired GEOGRAPHY column.

NEW QUESTION # 42
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