DAA-C01 Prüfungsfragen Prüfungsvorbereitungen, DAA-C01 Fragen und Antworten, SnowPro Advanced: Data Analyst Certification Exam

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q26-Q31):

26. Frage
How do Snowsight dashboards facilitate the presentation of data for business use analyses?

A. They enable diverse data representation for effective analyses.
B. Snowsight doesn't support visual data representation.
C. Snowsight limits data representation options, hindering analyses.
D. Snowsight dashboards are exclusively text-based, limiting analyses.

Antwort: A

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

27. Frage
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?

A. Creating a standard Snowflake view directly querying 'SALES DATA' and connecting Tableau to that view.
B. Creating a materialized view on top of 'SALES_DATA' , pre-aggregating sales data by REGION' and 'PRODUCT_CATEGORY , and connecting Tableau to the materialized view.
C. Using a stored procedure in Snowflake to calculate and store aggregated sales data in a separate table, and connecting Tableau to this aggregated table.
D. Using Tableau's data extract feature to import all 'SALES DATA into a Tableau Hyper file and connecting the dashboard to the extract.
E. Implementing Snowflake's Data Marketplace to source external sales data, which Tableau can then directly connect to without needing to access the company's 'SALES DATA'.

Antwort: B

Begründung:
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.

28. Frage
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?

A. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.
B. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.
C. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.
D. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
E. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.

Antwort: A

Begründung:
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.

29. Frage
You are tasked with creating a stored procedure in Snowflake to perform data cleansing on a table named 'CUSTOMER DATA'. The procedure should: 1) Remove rows where the 'EMAIL' column is NULL or empty. 2) Standardize the 'PHONE NUMBER' column by removing all non-numeric characters and ensuring it's exactly 10 digits long. 3) Return the number of rows removed due to invalid emails and the number of rows modified due to phone number standardization. Assume the table already exists and contains columns 'CUSTOMER (INT), (VARCHAR), and 'PHONE NUMBER (VARCHAR). Which of the following code snippets correctly implements this stored procedure? The procedure should use exception handling to gracefully handle errors, returning -1 for both counts if any error occurs.

A.
B.
C.
D. None of the above.
E.

Antwort: E

Begründung:
Option A is correct because it uses SQL to perform the data cleansing tasks, correctly utilizes 'SQLROWCOUNT' to capture the number of affected rows, and returns the results as a VARIANT OBJECT. It also includes proper exception handling. Options B, C, and D have errors in syntax or logic regarding return types, variable declaration, or how to retrieve row counts. Specifically, using Javascript or returning an ARRAY/TABLE when VARIANT is more flexible in this scenario.

30. Frage
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?

A. Implement custom error handling in your application code to pre-validate JSON records before uploading them to the stage.
B. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.
C. Use the 'VALIDATION MODE = RETURN ALL ERRORS parameter in the 'COPY INTO' statement and then filter the data based on the errors returned.
D. Use the 'ON ERROR = 'CONTINUE'' option in the 'COPY INTO' statement used by the Snowpipe definition in conjunction with the 'VALIDATE function to capture error details.
E. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.

Antwort: D

Begründung:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.

31. Frage
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