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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions
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%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Prepare and Present Data	10–15%	- Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results
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%	- Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs
Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
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. 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.
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. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.
E. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.

Answer: B

Explanation:
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.

NEW QUESTION # 41
What actions are typically involved in working with and querying data in Snowflake? (Select all that apply)

A. Leveraging materialized views for aggregations
B. Identifying and handling data anomalies
C. Using randomization techniques
D. Employing time travel for data retrieval

Answer: A,B,C,D

Explanation:
Working with Snowflake data involves identifying anomalies, using randomization, employing time travel for historical data retrieval, and utilizing materialized views for enhanced query performance.

NEW QUESTION # 42
What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)

A. Inserting new data
B. Deleting data entirely
C. Merging data from multiple tables
D. Updating existing data

Answer: A,B,D

Explanation:
General DML operations in Snowflake include inserting, updating, and deleting data.

NEW QUESTION # 43
Consider a scenario where you're analyzing website user behavior data in Snowflake. You have a table named 'user sessionS with a column containing semi-structured data (VARIANT type) describing user interactions during a session. You need to create a UDF that accepts and extracts all the distinct event types that occurred within that session. The UDF should return an array of unique event type strings. This array will be used later to identify users who have participated in a specific combination of events. Which of the following approaches can effectively achieve this using Snowflake's SQL extensibility features?

A. Creating a SQL UDF that utilizes Snowflake's ARRAY AGG and DISTINCT functions to aggregate all event types from the session data into an array, ensuring uniqueness. (Single Correct Answer)
B. Implementing a Java UDF that parses the session data, extracts the event types, uses Java's HashSet to guarantee uniqueness, and returns the HashSet as a comma-separated string. (Single Correct Answer)
C. Using an external function with a remote service to process 'session_data' and return JSON array with distinct event types. The external function setup must include API integration with the third party service and Role based access control. (Single Correct Answer)
D. Using a JavaScript UDF that iterates through the session data, extracts the event types, adds them to a JavaScript Set to ensure uniqueness, and then converts the Set to an array. This array is returned as a result. (Single Correct Answer)
E. All of the above can satisfy the question. (Single Correct Answer)

Answer: D

Explanation:
The most efficient and appropriate solution is A. A JavaScript UDF allows for direct manipulation of the session data (VARIANT) and leveraging JavaScript's Set object for efficient uniqueness enforcement before returning the result as an array. This minimizes data transformation overhead within Snowflake's SQL engine. B: While SQL UDFs are an option, processing nested data and enforcing uniqueness within SQL can be less efficient compared to JavaScript's built-in capabilities. C: Using java, while possible, add overhead to the processing since Java UDF requires to setup class definitions and imports which can be an overkill for this use case. D: This is an external API integration which has extra overhead and latency. E: Is incorrect because all approaches have tradeoffs and can be implemented in certain instances based on requirements.

NEW QUESTION # 44
How does enriching data through Snowflake Marketplace benefit data analysis? (Select all that apply)

A. Increases data redundancy
B. Facilitates better data quality
C. Supports better data normalization
D. Expands data sources for correlation

Answer: B,D

Explanation:
Enriching data through the marketplace expands data sources for correlation and enhances overall data quality.

NEW QUESTION # 45
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