Actual Snowflake DAA-C01 Exam Questions and Answers

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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 Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis
Perform Simple Data Transformations for Analysis	15–20%	- Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets
Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - 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%	- Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting
Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?

A. Regular views simplify complex data structures for better query performance.
B. They offer real-time updates reflecting instantaneous database changes.
C. Materialized views provide precomputed snapshots, improving query performance.
D. Materialized views restrict data retrieval for improved security.

Answer: C

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

NEW QUESTION # 34
A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?

A. select avg(total_income), state from taxpayer_wages group by state having state = :state;
B. select avg(total_income), state from taxpayer_wages group by state having state = state;
C. select avg(total_income), state from taxpayer_wages group by state having state = @state;
D. select avg(total_income), state from taxpayer_wages group by state having state = 'state';

Answer: A

Explanation:
In Snowsight, Snowflake's modern web interface, Filters are used to make dashboards and worksheets interactive. When a Data Analyst creates a custom filter, they define a "SQL Keyword" that acts as a variable within their SQL code. This allows users to select a value from a dropdown or date picker in the UI, which then dynamically updates the query's results.
The standard syntax for referencing a filter variable in a Snowflake worksheet or dashboard is the colon prefix (:) followed by the keyword name. In this specific scenario, the keyword is state. Therefore, the query must use :state to tell Snowflake that this value should be supplied by the dashboard's filter UI rather than being a literal string or a column reference.
Evaluating the Options:
* Option A is the 100% correct syntax. It correctly uses the :state identifier to link the query logic to the dashboard filter.
* Option B is incorrect because the @ symbol is typically used in Snowflake to reference stages (e.g.,
@mystage), not dashboard filters.
* Option C is incorrect because state = state is a tautology that will always evaluate to true for non-null values; it does not reference an external filter.
* Option D is incorrect because 'state' is a string literal. The query would literally look for a state named
"state" rather than using the user's selection.
Using these filters effectively is a core requirement for the Data Presentation and Data Visualization domain, as it enables the creation of self-service analytics tools where non-technical users can explore data subsets without modifying SQL code.

NEW QUESTION # 35
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. 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.
B. 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.
C.
D. 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.
E. 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.

Answer: A,E

Explanation:
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.

NEW QUESTION # 36
Which of the following is a key step in data preparation?

A. Data normalization
B. Algorithm selection
C. Model deployment
D. Visual analysis

Answer: A

NEW QUESTION # 37
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?

A. Constraints limit table join operations to specific data types
B. Primary keys allow only one-to-one table joins
C. Constraints restrict data access to authorized user roles
D. Primary keys ensure data uniqueness for table relationships

Answer: D

Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.

NEW QUESTION # 38
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