DAA-C01최신시험예상문제모음시험기출자료

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Data Modeling and Performance Optimization	- Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas - Performance tuning 1. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques
Security, Governance, and Data Sharing	- Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC) - Data sharing and governance 1. Secure data sharing 2. Data masking and policies
Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer - Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute
Data Loading and Unloading	- Data export 1. UNLOAD and external stages - Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts
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

>> DAA-C01최신 시험 예상문제모음 <<

DAA-C01최고덤프, DAA-C01덤프샘플문제 다운

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최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q11-Q16):

질문 # 11
A Data Analyst is working with three tables:

Which query would return a list of all brokers, a count of the customers each broker has. and the total order amount of their customers (as shown below)?

정답：B

설명：
To achieve the desired result, an analyst must understand the fundamental behavior of different JOIN types within Snowflake and how they affect the retention of records from the "left" or primary table. The goal here is to list all brokers, even those who have zero customers (like "Drew") or customers with zero orders (like
"Debby").
In SQL, an INNER JOIN only returns rows when there is a match in both tables. If we were to use an INNER JOIN between BROKER and CUSTOMER, Drew would be excluded from the results because he has no associated records in the CUSTOMER table. Similarly, an INNER JOIN with the ORDERS table would exclude any broker whose customers haven't placed an order.
Evaluating the Join Logic:
* Option C is the correct solution because it utilizes a chain of LEFT JOINs. A LEFT JOIN (or LEFT OUTER JOIN) ensures that every record from the left table (BROKER) is preserved in the result set. If no matching record exists in the joined table (CUSTOMER or ORDERS), Snowflake populates the columns with NULL. This is why "Drew" appears with a CUST_COUNT of 0 and "Debby" appears with a NULL for the total order amount.
* Option A fails because it uses an INNER JOIN for the CUSTOMER table, which would immediately filter out "Drew."
* Option B and Option D fail because they use INNER JOINs at different stages of the query, which would strip away brokers or customers that do not have matching order activity.
Additionally, the query correctly uses COUNT(DISTINCT c.customer_id) to ensure that customers are not double-counted if they have multiple orders, and GROUP BY 1 (referencing b.broker_name) to aggregate the data at the broker level. This pattern is essential for accurate Data Analysis in Snowflake when dealing with
"optional" relationships in a star or snowflake schema.

질문 # 12
Configuring subscriptions and updates in a dashboard tool primarily helps in:

A. Reducing the size of the data
B. Enhancing the security of the data
C. Ensuring users receive the latest data
D. Changing the dashboard layout

정답：C

질문 # 13
How do diverse chart types (e.g., bar charts, scatter plots, heat grids) contribute to effective data presentation and visualization in reports and dashboards?

A. Charts don't impact data visualization in reports or dashboards.
B. Different chart types offer varied data representation for better analysis.
C. Diverse chart types restrict data exploration in reports and dashboards.
D. They limit data representation options for simplicity.

정답：B

설명：
Different chart types offer varied data representation, aiding better analysis in reports and dashboards.

질문 # 14
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 Snowflake Pipe that automatically ingests data from the view into the 'STOCK_PRICES table whenever new data is available.
B. 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'
C. Create a Snowflake Task that runs daily and executes a simple 'INSERT INTO STOCK_PRICES SELECT FROM statement.
D. 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.
E. 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.

정답：B

설명：
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

질문 # 15
When conducting diagnostic analysis, why is it essential to collect related data and demographics?

A. To overlook statistical trends in historical data
B. It assists in identifying reasons/causes of anomalies in historical data
C. To restrict analysis to recent data only
D. To focus solely on isolated anomalies

정답：B

설명：
Collecting related data and demographics aids in identifying reasons/causes behind anomalies in historical data.

질문 # 16
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