DAA-C01試験の準備方法｜素晴らしいDAA-C01学習範囲試験｜効果的なSnowPro Advanced: Data Analyst Certification Exam資格準備

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Perform Descriptive and Diagnostic Analysis10–15%- Exploratory and ad-hoc analysis
- Anomaly detection and root cause analysis
- Statistical summarization and trend analysis
Topic 2: Prepare and Load Data15–20%- File formats: CSV, JSON, Parquet, Avro
- External tables and data validation
- Data ingestion methods: COPY INTO, stages, Snowpipe
Topic 3: Perform Simple Data Transformations for Analysis15–20%- Data cleansing, standardization, type conversion
- Views, materialized views, CTEs
- Handling NULLs and structuring datasets
Topic 4: Perform Predictive Analysis5–10%- Using Snowflake ML and built-in analytics
- Forecasting and predictive modeling
Topic 5: Prepare and Present Data10–15%- Snowsight dashboards and sharing results
- Align outputs with business requirements
- Data visualization and reporting
Topic 6: Use Built-in Functions and Create UDFs10–15%- User-Defined Functions (UDFs)
- Scalar, aggregate, table, system functions
Topic 7: Build and Troubleshoot Advanced SQL Queries20–25%- Semi-structured data processing
- Complex joins, subqueries, window functions
- Query optimization and troubleshooting

>> DAA-C01学習範囲 <<

Snowflake DAA-C01 Exam | DAA-C01学習範囲 - 信頼できるプロバイダ DAA-C01: SnowPro Advanced: Data Analyst Certification Exam 試験

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q44-Q49):

質問 # 44
A logistics company needs to determine which warehouses are within a 50km radius of a new distribution center. The warehouse locations are stored in a table 'WAREHOUSES' with columns 'WAREHOUSE ID' ONT), (GEOGRAPHY) and the distribution center's location is stored in a variable of type GEOGRAPHY. Which query will efficiently identify all warehouses within the specified radius, returning the 'WAREHOUSE ID and distance in kilometers?

正解：A

解説：
The correct answer uses 'ST DWITHIN' with the correct parameters and unit. 'ST DWITHIN(LOCATION, @distribution_center, 50000)' correctly filters the warehouses based on the 50km (50000 meters) radius. ST_DISTANCE calculates the distance in meters, which is then converted to kilometers by dividing by 1000. The warehouse location should come first followed by the distribution centre in the DWITHIN' Function.


質問 # 45
How do automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?

正解：B

解説：
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.


質問 # 46
When performing a diagnostic analysis, which actions aid in collecting related data to identify anomalies? (Select all that apply)

正解：C

解説：
Collecting data from various sources aids in comprehensive analysis, assisting in identifying anomalies.


質問 # 47
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?

正解：D

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


質問 # 48
You are investigating a performance bottleneck in a frequently used Snowflake query. You suspect the bottleneck might be due to data skew in a particular table. Which Snowflake system function and query combination would be the MOST efficient way to collect data to diagnose data skew on the 'orders' table, specifically for the column?

正解：D

解説：
Option B directly addresses data skew by grouping by 'order_date' and counting occurrences, then ordering by count in descending order to show the most frequent dates. The LIMIT 10 clause ensures that only the dates with the highest counts are returned, improving efficiency. Option A only provides an approximate distinct count, not showing the distribution. Option C is used to estimate query cost and not identify data skew. Option D identifies dates with counts above average, which doesn't necessarily indicate skew in the TOP most frequent dates. Option E only shows table properties.


質問 # 49
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