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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Aggregate functions
  • 2. Snowflake-specific analytical features
  • 3. Time-series analysis
Topic 2: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Common Table Expressions (CTEs)
  • 3. Window functions
  • 4. QUALIFY clauses
- Design data models
  • 1. Star schema design
  • 2. Data vault models
  • 3. Snowflake schema design
Topic 3: Data Ingestion and Data Preparation15%-20%- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
  • 2. Find external data sets that correlate with available data
  • 3. Create tables and views
- Use a collection system to retrieve data
  • 1. Retrieve data from unstructured sources
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from structured sources (CSV)
  • 4. Synthetic Data Generation
- 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
- 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
- Perform data discovery to identify what is needed from available datasets
  • 1. Determine the level of data granularity required
  • 2. Identify elements required for business goals using BI reports or SQL analysis
  • 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 4. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 5. Query tables to assess data elements and statistics maintained by Snowflake
- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
Topic 4: 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. Snowsight dashboards
  • 2. Present analytical results

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최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q29-Q34):

질문 # 29
What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?

정답：D

설명：
When working with "Big Data" at the scale of trillions of rows, calculating an exact count of unique values using COUNT(DISTINCT column) is extremely resource-intensive. This is because Snowflake must keep track of every unique value encountered to ensure no duplicates are counted, leading to high memory usage and long execution times (often referred to as "spilling to disk").
To solve this, Snowflake provides HyperLogLog (HLL) functions. HLL(*) (or specifically HLL_ACCUMULATE and HLL_ESTIMATE) allows an analyst to estimate the cardinality (the number of unique elements) with a very small, known margin of error (typically around 1%). This is significantly faster and uses far fewer credits than an exact count because it uses a probabilistic algorithm rather than a state- heavy tracking mechanism.
Evaluating the Options:
* Option A is technically correct for small datasets but is highly inefficient for trillions of rows, directly contradicting the "efficiently" requirement of the question.
* Option C is a distractor; while Snowflake has various SYSTEM$ functions, SYSTEM$ESTIMATE is not a standard function for cardinality.
* Option D is a formula that doesn't target cardinality but rather a ratio (density).
* Option B is the correct answer. The HLL family of functions is the industry standard within Snowflake for high-performance cardinality estimation on massive datasets.


질문 # 30
A retail company wants to visualize sales performance across different product categories and regions. The business stakeholders need to identify both overall sales trends and granular insights into the performance of specific products in specific regions. They require a dashboard that allows for easy comparison of sales across categories and regions, highlighting best and worst performers. Which combination of chart types would be MOST effective for this dashboard, considering scalability and the need to avoid over-plotting?

정답：C

설명：
A heat grid effectively visualizes the relationship between two categorical variables (category and region) using color intensity, making it easy to identify high and low sales areas. A time series chart is appropriate for displaying trends over time. A treemap shows the proportional size of each category contributing to total sales. Stacked bar charts can become difficult to read with many categories and pie charts are not ideal for precise comparisons. Scatter plots are useful for correlation analysis (Sales vs Profit). A map would be good for high level visualization but not for specific numbers or precise details. Bullet charts are more suitable for target vs actual comparisons than a regional overview.


질문 # 31
You are tasked with optimizing a dashboard displaying sales performance across different regions. The dashboard queries a large table called 'SALES DATA, which is partitioned by 'SALE DATE. The dashboard loads very slowly. The data analyst team notices that queries for the dashboard often filter on a specific region and a date range. Which of the following steps can you take to improve the dashboard's performance? (Select TWO)

정답：B,C

설명：
Creating a materialized view (A) pre-aggregates the data, significantly reducing the amount of data that needs to be processed for each dashboard refresh. Reclustering on 'REGIONS and (D) physically organizes the data on disk based on these frequently used filter columns, improving query performance by minimizing the need to scan irrelevant partitions. Table clones (B) provide a separate copy of the data but don't inherently improve query performance. Search optimization (C) is useful for point lookups, but not as effective for date range filtering in this scenario. Increasing warehouse size (E) helps, but optimizing the data structure offers a more fundamental and cost-effective improvement.


질문 # 32
An organization needs to provide daily summaries of key metrics to its executive team via email. They currently have a complex SQL query that generates the report, but the query takes several minutes to run. The executive team needs the data before 8 AM daily. The data warehouse is also used for other reporting and analytics tasks throughout the day. Given these constraints, what is the MOST efficient and reliable approach to operationalize this report?

정답：C

설명：
Creating a materialized view (B) is the most efficient approach. Materialized views pre-compute the results, so the nightly task refreshes the pre-calculated data. This ensures fast report generation before 8 AM, even with a complex underlying query. Running the complex query directly (A) might exceed the time limit. Using a third-party ETL tool (C) adds unnecessary complexity. Snowpipe (D) is for continuous data ingestion, not report generation. Data Marketplace (E) is unlikely to perfectly match the custom needs.


질문 # 33
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?

정답：C

설명：
Materialized views provide precomputed snapshots, enhancing query performance.


질문 # 34
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