DAA-C01시험대비인증덤프자료 - DAA-C01인기자격증

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

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Snowflake DAA-C01인기자격증 - DAA-C01참고자료

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최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q56-Q61):

질문 # 56
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?

A. Implement Snowpipe for initial data ingestion, complemented by Snowflake's clustering feature based on timestamp to optimize historical analysis queries. And employ a stream processing engine to perform time window pre-aggregation.
B. Utilize an external stream processing engine to pre-aggregate the data into time windows (e.g., I-minute, I-hour) before ingesting into Snowflake using Snowpipe.
C. Ingest data directly into a single Snowflake table using Snowpipe with JSON data type. Create separate materialized views for real-time dashboards and historical trend analysis.
D. Employ a combination of Snowpipe for near real-time data ingestion into a raw table, and then use Snowflake's Search Optimization Service for faster queries.
E. Use Snowpipe to ingest data into a raw landing table, and then use Snowflake tasks to transform and load the data into separate tables optimized for real-time dashboards and historical analysis.

정답：A,E

설명：
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.

질문 # 57
You are tasked with building a near real-time data pipeline that ingests streaming data from Kafka into Snowflake. The data is semi- structured JSON. The goal is to transform and load only records with a 'status' field equal to 'active' into a table named 'ACTIVE RECORDS'. Which approach provides the most efficient and scalable solution, minimizing latency and Snowflake resource consumption?

A. Ingest all data into a raw table using Snowpipe. Create a materialized view on top of the raw table, filtering for 'status' = 'active', and store the results in 'ACTIVE RECORDS'.
B. Implement a Snowpipe with a continuous ingest endpoint. Use a transformation defined within the COPY INTO statement to filter for 'status' = 'active' during the load process.
C. Create an external table pointing to the Kafka topic. Use a Snowflake stream on the external table to track changes, and a task to transform and load 'active' records into 'ACTIVE RECORDS'
D. Use Snowflake's Kafka connector to load all data into a staging table, then create a scheduled task that runs every minute to transform and load the 'active' records into 'ACTIVE RECORDS
E. Use a third-party ETL tool to consume the Kafka stream, filter for 'status' = 'active', and then load the data directly into 'ACTIVE RECORDS' using the Snowflake JDBC driver.

정답：B

설명：
Snowpipe with a transformation in the COPY INTO statement offers the most efficient and scalable near real-time data ingestion solution. This approach allows filtering during the load process, avoiding the need to load all data and then filter later. Option A involves a scheduled task, which introduces latency. Option C's external table + stream approach is generally slower for continuous ingest. Option D adds the overhead and cost of a 3rd party ETL tool, while Option E's materialized view would work, it isn't the most efficient approach for the initial data filtering.

질문 # 58
When employing different modeling techniques for the consumption layer in Snowflake (e.g., dimensional, Data Vault), what factors influence the choice between these techniques?

A. Data consistency requirements
B. Complexity of data transformations
C. Availability of specific database object types
D. Preference for optimized query performance

정답：A,B,D

설명：
Choosing modeling techniques depends on factors like data transformation complexity, consistency requirements, and preferences for query performance optimization when designing the consumption layer in Snowflake.

질문 # 59
Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?

A. Materialized view
B. Stored procedure
C. User-Defined Function (UDF)
D. Common Table Expressions (CTE)

정답：B

설명：
In Snowflake, a Stored Procedure is the primary object used for procedural logic and the encapsulation of multiple SQL statements. Unlike standard functions that are typically restricted to returning a single value or a table based on an input, Stored Procedures are designed to perform administrative tasks, wrap complex business logic, and execute a sequence of operations-such as DDL (Data Definition Language) and DML (Data Manipulation Language) commands-within a single callable object.
Modular code development is facilitated by Stored Procedures because they allow analysts and developers to write logic once and reuse it across various workflows. For example, a procedure can be written to truncate a staging table, call a COPY INTO command, and then execute several INSERT statements into a final fact table. This "wrapper" approach ensures consistency, as any change to the logic only needs to be updated in the procedure definition rather than in every script or task that uses it.
Evaluating the Options:
* Option B is incorrect because a Materialized View is a pre-computed result set used to improve query performance; it does not "encapsulate a series of statements" for procedural execution.
* Option C is incorrect because while User-Defined Functions (UDFs) provide reusability, they are generally intended for calculations or data transformations that return a result. They are much more restricted than procedures; for instance, a UDF cannot execute DDL commands like CREATE TABLE.
* Option D is incorrect because a Common Table Expression (CTE) is a temporary named result set defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement. It is not a persistent database object and cannot be reused across different sessions or scripts.
* Option A is the correct answer. Snowflake Stored Procedures can be written in multiple languages (JavaScript, Snowflake Scripting/SQL, Python, Java, or Scala), providing the flexibility needed for sophisticated, modular automation in a data pipeline.

질문 # 60
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

정답：A

설명：
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.

질문 # 61
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