Snowflake DAA-C01最新日本語版参考書、DAA-C01日本語試験情報

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Transformation and Analysis	- SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions - Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching
Topic 2: Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute - Snowflake architecture concepts 1. Cloud services layer, compute layer, storage layer 2. Virtual warehouses and scaling
Topic 3: Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization
Topic 4: Data Loading and Unloading	- Data ingestion methods 1. Continuous ingestion and Snowpipe concepts 2. COPY INTO and bulk loading - Data export 1. UNLOAD and external stages
Topic 5: 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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q39-Q44):

質問 # 39
You have a Snowflake table named 'CUSTOMER DATA with a 'JOIN DATE column currently stored as VARCHAR. You need to convert this column to a DATE data type. However, the 'JOIN DATE column contains various date formats, including 'YYYY-MM-DD', 'MM/DD/YYYY', and some invalid date strings like 'UNKNOWN'. Which combination of Snowflake SQL functions and techniques provides the MOST robust solution to convert the column to a DATE data type while handling invalid values gracefully?

A. Use 'YYYY-MM-DD')' along with error handling using 'BEGIN...EXCEPTION...END block to capture conversion errors and update the corresponding rows to a default date.
B. Create multiple temporary tables for each date format using 'TO DATE, then union them together, handling the conversion errors through separate logic.
C. Use 'CASE statements with 'TRY TO DATE and format strings to handle multiple date formats and assign a default value to rows that cannot be converted to a date.
D. Use 'YYYY-MM-DD')' , then update any resulting NULL values with a default date like '1900-01-01
E. Use 'TO DATE(JOIN DATE)' and handle errors during the conversion process by correcting invalid date formats manually.

正解：C

解説：
Option C offers the most robust solution. Using 'CASE' statements with 'TRY TO DATE' allows you to specify multiple format strings to handle the different date formats present in the 'JOIN_DATE column. The function gracefully handles invalid date strings by returning NULL, which can then be replaced with a default value using the 'ELSE clause in the 'CASE' statement. This approach avoids errors during conversion and ensures that all rows have a valid date value or a meaningful default.

質問 # 40
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

A. Both policies restrict data visibility for better security.
B. Row access policies limit data visibility based on user privileges.
C. Dynamic Data Masking doesn't affect data visibility in dashboards.
D. They improve data visibility for all users without restrictions.

正解：B

解説：
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.

質問 # 41
You have a large dataset of IoT sensor readings stored in compressed JSON files within an AWS S3 bucket. Each JSON file contains an array of sensor readings with the following structure:
You need to load this data into a Snowflake table named 'sensor data' with columns 'sensor id', 'timestamp', 'temperature', and 'humidity'. Which of the following Snowflake commands would be the MOST efficient and appropriate to ingest this data, assuming you have already created the table and a named stage pointing to the S3 bucket?

A. Option E
B. Option C
C. Option A
D. Option D
E. Option B

正解：C

解説：
Option A is the most efficient and direct way to load the data. = TRUE' correctly handles the array of JSON objects within each file. Option B would fail because it doesn't strip the outer array. Option C is incorrect as it treats the JSON as CSV. Option D, although functional, involves creating a temporary table and flattening, making it less efficient. Option E is invalid syntax, and unnecessarily complex.

質問 # 42
A data analyst needs to enrich customer data in a Snowflake database with demographic information obtained from the Snowflake Marketplace. The purchased listing provides data as a secure view Which of the following SQL commands is the MOST efficient and secure way to create a new table in the data analyst's database that combines customer data with the demographic information from the Marketplace listing, while ensuring that only necessary columns from the Marketplace data are included?

正解：C

解説：
Option D is the most efficient and secure because: it creates a new table or replaces if one exists, it explicitly selects only the necessary columns (age, income) from the Marketplace view, preventing unnecessary data exposure. It also explicitly uses JOIN condition. A new table has to created instead of view for persistence and to prevent recomputing of the data on frequent requests.

質問 # 43
How do Snowsight dashboards enable effective data presentation for business use analyses?

A. Snowsight dashboards rely solely on text-based representations.
B. Snowsight offers limited data representation options.
C. They enable diverse data representation for effective analyses.
D. Snowsight doesn't support visual data representation.

正解：C

解説：
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.

質問 # 44
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