DAA-C01 Examsfragen, DAA-C01 Prüfungsaufgaben

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Security, Governance, and Data Sharing	- Data sharing and governance 1. Secure data sharing 2. Data masking and policies - Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC)
Data Transformation and Analysis	- SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions - Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics
Data Loading and Unloading	- Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts - Data export 1. UNLOAD and external stages
Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q16-Q21):

16. Frage
How do aggregate functions differ from window functions in Snowflake?

A. Aggregate functions return single values.
B. Window functions modify table structures.
C. Aggregate functions operate on groups of rows.
D. Window functions work on entire tables.

Antwort: A,C

Begründung:
Aggregate functions operate on groups of rows and return single values, distinguishing them from window functions.

17. Frage
You are designing a data warehouse for a retail company. The "SALES table stores transaction data and includes columns like 'TRANSACTION ID', 'PRODUCT ID', 'CUSTOMER ID, 'SALE DATE', and 'SALE AMOUNT'. The 'PRODUCT ID' references the 'PRODUCTS table, and 'CUSTOMER references the 'CUSTOMERS' table. Which of the following strategies represent the MOST optimal approach to define primary keys in this scenario, considering Snowflake's best practices and the need for efficient query performance, assuming 'TRANSACTION ID' is globally unique?

A. Define 'TRANSACTION as the primary key on the 'SALES' table, 'PRODUCT ID as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID' as the primary key on the 'CUSTOMERS' table.
B. Define a composite primary key on the 'SALES' table consisting of 'TRANSACTION D', 'PRODUCT ID, and 'CUSTOMER ID. Define 'PRODUCT ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER_ID' as the primary key on the 'CUSTOMERS' table.
C. Do not define primary keys on any of the tables. Rely solely on Snowflake's internal optimizations.
D. Define ' TRANSACTION_ID' as the primary key on the 'SALES' table. Define 'PRODUCT_ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID as the primary key on the 'CUSTOMERS table. Then, create unique indexes on 'PRODUCT ID in 'SALES referencing 'PRODUCTS and in referencing 'CUSTOMERS'.
E. Define as the primary key on the 'SALES' table. Do not define primary keys on 'PRODUCTS' or 'CUSTOMERS'

Antwort: A

Begründung:
While Snowflake does not enforce primary key constraints, defining them provides valuable metadata for the query optimizer. Since TRANSACTION_ID' is unique in 'SALES', it is a suitable primary key. Defining primary keys on 'PRODUCTS' and 'CUSTOMERS' is also appropriate for their respective tables. Creating unique indexes is redundant if primary keys are defined. Skipping primary key definitions entirely can hinder optimization. A composite key in 'SALES' is unnecessary as 'TRANSACTION_ID is already globally unique.

18. Frage
You are tasked with loading multiple CSV files from an external stage into a Snowflake table. The files have the following naming convention: HHMlSS.csv' (e.g., 'data_20240101 120000.csv'). You need to load all the files that were created on or after '2024-01-01' into the 'target_table'. Which of the following approaches can be used to load those data?

A. Using a single COPY INTO statement with a wildcard pattern in the file path and filtering in a subsequent INSERT INTO statement using a WHERE clause on the filename derived from METADATA$FILENAME.
B. Using a Snowpipe with a file notification integration, configuring the notification to trigger only for files created on or after '2024-01-01'.
C. Using a single COPY INTO statement with a pattern match against METADATA$FILENAM The matching logic is done inline with the COPY INTO statement using regex.
D. Using a loop with dynamic SQL to construct COPY INTO statements for each matching file based on the date substring.
E. Using a Snowflake task that runs daily, retrieves all the file names from the stage, filters the file names based on created date and loops through each filtered file name to load them

Antwort: A,C

Begründung:
Option B is a viable solution. COPY INTO can be combined with METADATA$FILENAME filtering to load files matching a criteria. Option E provides a better, more efficient solution that leverages the 'METADATA$FILENAME' pseudo-column to filter files directly within the 'COPY INTO' statement using regular expressions or string functions to extract and compare the date from the filename. Option A will work but is very inefficient. Option C will work, but complex to maintain. Option D is more complex to setup than option E. Snowpipe generally does not accept a single COPY command but rather an ongoing copy operation

19. Frage
You have a Snowflake table named 'CUSTOMER DATA with a 'JOIN DATE column currently stored as VARCHAR. You need to convert this column to a DATE data type. However, the 'JOIN DATE column contains various date formats, including 'YYYY-MM-DD', 'MM/DD/YYYY', and some invalid date strings like 'UNKNOWN'. Which combination of Snowflake SQL functions and techniques provides the MOST robust solution to convert the column to a DATE data type while handling invalid values gracefully?

A. Use 'CASE statements with 'TRY TO DATE and format strings to handle multiple date formats and assign a default value to rows that cannot be converted to a date.
B. Create multiple temporary tables for each date format using 'TO DATE, then union them together, handling the conversion errors through separate logic.
C. Use 'TO DATE(JOIN DATE)' and handle errors during the conversion process by correcting invalid date formats manually.
D. Use 'YYYY-MM-DD')' , then update any resulting NULL values with a default date like '1900-01-01
E. Use 'YYYY-MM-DD')' along with error handling using 'BEGIN...EXCEPTION...END block to capture conversion errors and update the corresponding rows to a default date.

Antwort: A

Begründung:
Option C offers the most robust solution. Using 'CASE' statements with 'TRY TO DATE' allows you to specify multiple format strings to handle the different date formats present in the 'JOIN_DATE column. The function gracefully handles invalid date strings by returning NULL, which can then be replaced with a default value using the 'ELSE clause in the 'CASE' statement. This approach avoids errors during conversion and ensures that all rows have a valid date value or a meaningful default.

20. Frage
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.

Antwort: D

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

21. Frage
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