DAA-C01日本語復習赤本 & DAA-C01復習教材

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Prepare and Load Data	15–20%	- File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation
Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements
Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q33-Q38):

質問 # 33
Why is the Parquet format preferred for complex data sets?

A. It has visually appealing data presentation
B. It changes data colors for differentiation
C. It randomly alters data for testing
D. It supports efficient compression and encoding schemes

正解：D

質問 # 34
A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?

A. select avg(total_income), state from taxpayer_wages group by state having state = 'state';
B. select avg(total_income), state from taxpayer_wages group by state having state = @state;
C. select avg(total_income), state from taxpayer_wages group by state having state = state;
D. select avg(total_income), state from taxpayer_wages group by state having state = :state;

正解：D

解説：
In Snowsight, Snowflake's modern web interface, Filters are used to make dashboards and worksheets interactive. When a Data Analyst creates a custom filter, they define a "SQL Keyword" that acts as a variable within their SQL code. This allows users to select a value from a dropdown or date picker in the UI, which then dynamically updates the query's results.
The standard syntax for referencing a filter variable in a Snowflake worksheet or dashboard is the colon prefix (:) followed by the keyword name. In this specific scenario, the keyword is state. Therefore, the query must use :state to tell Snowflake that this value should be supplied by the dashboard's filter UI rather than being a literal string or a column reference.
Evaluating the Options:
* Option A is the 100% correct syntax. It correctly uses the :state identifier to link the query logic to the dashboard filter.
* Option B is incorrect because the @ symbol is typically used in Snowflake to reference stages (e.g.,
@mystage), not dashboard filters.
* Option C is incorrect because state = state is a tautology that will always evaluate to true for non-null values; it does not reference an external filter.
* Option D is incorrect because 'state' is a string literal. The query would literally look for a state named
"state" rather than using the user's selection.
Using these filters effectively is a core requirement for the Data Presentation and Data Visualization domain, as it enables the creation of self-service analytics tools where non-technical users can explore data subsets without modifying SQL code.

質問 # 35
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

A. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.
B. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
C. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.
D. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.
E. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.

正解：E

解説：
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

質問 # 36
How does using Snowsight's data loading capabilities impact the overall data preparation process?

A. Streamlines data loading, reducing preparation time
B. Limits data loading to specific file formats
C. Doesn't support batch data loading
D. Increases data transformation complexities

正解：A

解説：
Snowsight's data loading capabilities streamline the process, reducing preparation time by facilitating efficient data loading.

質問 # 37
You have a large dataset of sensor readings stored as Parquet files in an external stage. The Parquet files are compressed using Snappy compression. You need to create a Snowflake table that allows you to efficiently query this data, minimizing storage costs and maximizing query performance. Which of the following options represents the MOST efficient approach, considering both storage and query performance, along with any configuration changes needed?

A. Create an external table pointing to the Parquet files with 'FILE_FORMAT = (TYPE = PARQUET, COMPRESSION = 'AUTO')'. Snowflake automatically detects the compression. Apply optimize table on the external table.
B. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly. Specify the 'FILE_FORMAT = (TYPE = PARQUET, COMPRESSION = 'SNAPPY')' option when creating the external stage. Create standard view on top of this external stage. Run analyze table on base table.
C. Create an external table without specifying any compression options. Snowflake automatically detects the compression. Create a materialized view on top of external stage.
D. Create a Snowflake table with a VARIANT column. Copy data into this table from the external stage specifying file format as '(TYPE = PARQUET)'. Create a materialized view based on the data present in the table with variant column.
E. Load the Parquet files directly into a Snowflake internal table using 'COPY INTO' without specifying any file format options. Snowflake will automatically handle Snappy compression and optimize for internal storage.

正解：D

解説：
Creating an external table and explicitly specifying the file format with 'COMPRESSION = 'SNAPPY" allows Snowflake to efficiently access the data. 'AUTO REFRESH = TRUE' ensures schema evolution is handled, and creating the external table directly allows you to leverage Parquet's columnar storage. 'ANALYZE TABLE allows Snowflake to optimize the query plan. While Snowflake can automatically detect compression in some cases, explicitly specifying it provides more control and can improve performance. 'OPTIMIZE TABLE' is not applicable to external tables. Materialized views on top of External tables is computationally expensive. Using variant column is not optimized approach as it bypasses the benefits of Parquet files.

質問 # 38
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