最も人気のあるDAA-C01オンライン試験だけが、SnowPro Advanced: Data Analyst Certification Examに合格することができます

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Other partner visualization tools 2. Tableau integration 3. Power BI integration - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Topic 2: Data Transformation and Data Modeling	22%-30%	- Design data models 1. Star schema design 2. Data vault models 3. Snowflake schema design - Transform data using SQL 1. Window functions 2. QUALIFY clauses 3. Common Table Expressions (CTEs) 4. PIVOT/UNPIVOT operations
Topic 3: Data Ingestion and Data Preparation	15%-20%	- Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use a collection system to retrieve data 1. Retrieve data from structured sources (CSV) 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Retrieve data from unstructured sources 4. Synthetic Data Generation - Implement data processing solutions 1. Respond to processing failures 2. Automate and implement data pipelines (scheduling) 3. Use logging and monitoring solutions (auditing, data lineage) 4. Cleanse, conform, and enrich data - 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 - Perform data discovery to identify what is needed from available datasets 1. Evaluate required transformations (table joins, set operations, ASOF JOINS) 2. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Identify elements required for business goals using BI reports or SQL analysis 5. Determine the level of data granularity required - 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
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q61-Q66):

質問 # 61
Which aspects are important when making predictions based on data for forecasting purposes?
(Select all that apply)

A. Using only basic arithmetic functions for forecasting
B. Considering trends and anomalies in historical data
C. Relying solely on historical data without considering external factors
D. Incorporating statistical methods for accurate predictions

正解：B、D

解説：
Incorporating statistical methods and considering trends and anomalies are crucial for accurate predictions in forecasting.

質問 # 62
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?

A. Use a third-party reporting tool (e.g., Tableau, Power Bl) to connect to Snowflake, create the report, and schedule it for daily email delivery with a CSV attachment. Configure the tool with the appropriate Snowflake connection details and credentials.
B. Create a Snowflake Alert that triggers when the 'WEB ACTIVITY table is updated. The alert executes a stored procedure that queries the data, formats it as CSV using Snowflake Scripting, writes the CSV to an internal stage, and then uses a Python UDF to send the email with the CSV attachment. Grant the alert necessary privileges.
C. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format it as CSV using Javascript UDF, writes the CSV to an internal stage, and then uses a Java UDF (Util.EmailSender) to send the email with the CSV attachment. Grant necessary permissions to the task's service account to access the stage and execute the UDFs.
D. Create a scheduled task in an external orchestrator (e.g., Airflow). This task uses the Snowflake Python connector to query the data, format it as CSV, writes the CSV to an external stage (e.g., AWS S3), and then uses an email service (e.g., AWS SES) to send the email with the CSV attachment. Configure appropriate IAM roles for Snowflake to access S3.
E. Create a Snowflake Stream on the 'WEB ACTIVITY table. When data changes are detected, trigger an external function (e.g., AWS Lambda) via a pipe. The Lambda function queries the data, formats it as CSV, uploads the file to S3, and sends an email with a download link.

正解：D

解説：
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.

質問 # 63
You need to create an exact, point-in-time copy of a production database named 'CUSTOMER DATA for testing purposes, without impacting the performance of the production system. You want to ensure that the test database 'CUSTOMER DATA TEST uses minimal storage. Which of the following Snowflake commands provides the MOST efficient way to achieve this?

正解：B

解説：
The 'CREATE DATABASE CUSTOMER DATA TEST CLONE CUSTOMER DATA;' command is the MOST efficient because it leverages Snowflake's zero-copy cloning feature. Cloning creates a metadata copy of the database; data is only physically copied when changes are made to the clone. This minimizes storage usage and has a minimal performance impact on the production database. Option A is used for replication across accounts/regions. Option B is not a valid Snowflake command. Option D only copies the structure, and not the data. Option E copies the data into a table within a new or existing database, not cloning the entire database structure.

質問 # 64
When automating and repeating data processing tasks, what challenges might be encountered in managing and sharing Snowsight dashboards effectively?

A. Limited user access control
B. Incompatibility with other BI tools
C. Ensuring real-time data updates
D. Difficulty in embedding external content

正解：A、D

解説：
Managing Snowsight dashboards might pose challenges related to limited user access control and embedding external content seamlessly within the dashboards.

質問 # 65
You are responsible for a Snowflake data pipeline that loads data from various external sources into a data warehouse. You have implemented Snowpipe for continuous data ingestion from AWS S3. One of your data sources is known to occasionally produce corrupted data files. You need to design a resilient pipeline that can handle these corrupted files without halting the entire data loading process and allows for the identification and quarantine of these files for further investigation. Assume you have a table called 'RAW DATA' and you are using a file format 'MY CSV FORMAT. Select TWO actions which best address this situation:

A. Implement a pre-processing step using a Snowflake task to validate the data in each file before it's loaded into the 'RAW_DATR table using a 'COPY INTO' statement. The Task would then move files failing validation to a quarantine bucket.
B. Set 'ON_ERROR = 'SKIP_FILE" in the 'COPY INTO' statement used by Snowpipe. This setting will skip entire files that contain errors, preventing them from being loaded into the table.
C. Create a separate stream on the 'RAW DATA' table that filters for records with errors and then move these erroneous records into a quarantine table using a task. Implement ERROR=CONTINIJE on the Copy Statement.
D. Configure Snowpipe's 'ERROR INTEGRATION' to log details of files that fail during ingestion, including the error messages. Then, set ERROR-CONTINUE" on the Copy Statement. This requires an external stage for the error logs. In addition, create a separate task that queries the ERROR INTEGRATION external stage and moves the corrupt files into a separate quarantine bucket.
E.

正解：A、D

解説：
Option B directly addresses the requirement for a resilient pipeline by utilizing 'ERROR INTEGRATION' to log errors and quarantine corrupt files. Setting ON_ERROR to CONTINUE avoids pipeline interruption, and creating a task to query the error stage and move files to a quarantine bucket achieves the desired outcome. Option C would be the 2nd best option because it provides for pre-processing and validation, which avoids loading the corrupted records into the RAW_DATA table in the first place. Option A only skips the file and doesn't log the errors or quarantine the file. Option D creates a stream on the RAW DATA table, but we would like to avoid writing erroneous records to the RAW DATA table in the first place. 'VALIDATE only checks the existing data, so it would not solve any problems in Data Load.

質問 # 66
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