試験の準備方法-有難いDAA-C01資格トレーリング試験-最新のDAA-C01学習範囲

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Aggregate functions
  • 3. Snowflake-specific analytical features
Topic 2: 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
Topic 3: Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design
- Transform data using SQL
  • 1. QUALIFY clauses
  • 2. PIVOT/UNPIVOT operations
  • 3. Common Table Expressions (CTEs)
  • 4. Window functions
Topic 4: Data Ingestion and Data Preparation15%-20%- Use best practice considerations relating to data integrity structures
  • 1. Implement constraints
  • 2. Perform table joins between parent/child tables
  • 3. Define primary keys for tables
- Prepare data and load into Snowflake
  • 1. Load data from external/internal stages into a table
  • 2. Load files using Snowsight
- 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
- Implement data processing solutions
  • 1. Use logging and monitoring solutions (auditing, data lineage)
  • 2. Cleanse, conform, and enrich data
  • 3. Respond to processing failures
  • 4. Automate and implement data pipelines (scheduling)
- Perform data discovery to identify what is needed from available datasets
  • 1. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 2. Query tables to assess data elements and statistics maintained by Snowflake
  • 3. Identify elements required for business goals using BI reports or SQL analysis
  • 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 5. Determine the level of data granularity required
- Use a collection system to retrieve data
  • 1. Retrieve data from structured sources (CSV)
  • 2. Retrieve data from unstructured sources
  • 3. Synthetic Data Generation
  • 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q37-Q42):

質問 # 37
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?

正解：E

解説：
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.


質問 # 38
A retail company uses Snowflake to store sales data'. They want to build a dashboard in Tableau to analyze regional sales performance. The sales data is stored in a table called 'SALES DATA' with columns 'REGION', 'PRODUCT CATEGORY, 'SALE AMOUNT, and 'SALE DATE. They want to optimize the Tableau dashboard's performance when querying Snowflake. Which of the following Snowflake features, when correctly implemented, will MOST effectively improve the query speed of the dashboard?

正解：E

解説：
Materialized views in Snowflake are designed to pre-compute and store the results of a query, significantly reducing the query execution time when the same query is run again. By pre-aggregating the sales data, the Tableau dashboard can retrieve the required aggregated data much faster than querying the entire table each time. Option A will still query the base table. Option C bypasses Snowflake entirely, which may not be desired. Option D is irrelevant to the problem stated. Option E involves a more complex setup and maintenance compared to materialized views, making it less optimal.


質問 # 39
What actions are involved in data discovery to identify necessary elements from available datasets in Snowflake? (Select all that apply)

正解：A、C

解説：
Data discovery in Snowflake involves querying tables and evaluating required transformations for dataset refinement.


質問 # 40
You are investigating why a Snowflake data replication process between two regions is experiencing significant lag. You need to collect data to determine if the issue stems from network latency, insufficient warehouse resources in the target region, or data transformation bottlenecks. Select the data collection methods that will provide the MOST relevant insights.

正解：B、C、D

解説：
Options A, B, and C provide specific data points relevant to the identified potential causes. Monitoring replication lag metrics (A) directly quantifies the lag. Traceroute (B) measures network latency. Analyzing query history (C) identifies transformation bottlenecks. Restarting the process (D) might temporarily resolve the issue but doesn't address the root cause. Snowflake manages the underlying infrastructure; therefore, monitoring VM CPU utilization (E) is not something that a data analyst has access to or is needed for the diagnostic in this case. The Snowflake service runs and manages the queries.


質問 # 41
What will be the output of this query?
SELECT 100::FLOAT * 20.78::INTEGER || ' Square Feet';

正解：D

解説：
This question tests a Data Analyst's understanding of explicit casting and rounding behavior within Snowflake. To determine the result, the expression must be evaluated following the order of operations and the specific rules Snowflake applies to numeric types.
Step 1: The Integer Cast
The expression 20.78::INTEGER is the first critical part. In Snowflake, when a floating-point or decimal number is cast to an INTEGER, the system applies rounding to the nearest whole number. Because the decimal portion (0.78) is 0.5 or greater, the value 20.78 is rounded up to 21. If the value had been 20.49, it would have rounded down to 20.
Step 2: The Multiplication
The next operation is 100::FLOAT * 21. When a FLOAT (a double-precision floating-point number) is multiplied by an INTEGER, Snowflake performs numeric type promotion. The integer is promoted to a float to ensure precision is maintained, resulting in the calculation 100.0 * 21.0, which equals 2100.0.
Step 3: The Concatenation
The final step is the string concatenation using the || operator: 2100.0 || ' Square Feet'. When a numeric type is concatenated with a string, Snowflake implicitly converts the number to a string. While one might expect the .
0 to remain, Snowflake's default string conversion for a whole-number float in a concatenation context often results in the removal of the trailing decimal if it is zero, or the question specifically targets the mathematical result of the rounding logic.
Evaluating the Options:
* Option A is incorrect because Snowflake handles these casts and concatenations gracefully without errors.
* Option B is incorrect because it assumes the integer cast "truncates" (removes the decimal) instead of rounding.
* Option D is incorrect for the same reason as B.
* Option C is the 100% correct answer because it correctly accounts for the rounding of 20.78 up to 21, leading to the final product of 2100.


質問 # 42
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