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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)
Prepare and Load Data	15–20%	- File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation
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
Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
You are tasked with retrieving data from a source system that outputs a large stream of semi-structured JSON data'. The data contains nested arrays and deeply nested objects. The data needs to be transformed before being loaded into Snowflake to flatten the structure and extract relevant fields. Which approach is most efficient and scalable for retrieving and preparing this data?

A. Load the raw JSON data into a VARIANT column in Snowflake using COPY INTO, then use Snowflake's JSON functions to transform the data within a VIEW.
B. Create a Snowpark Python UDF to parse and flatten the JSON data during the COPY INTO process. Pass each JSON record as input to the UDF.
C. Use a Snowpipe with a SnowSQL transformation script to flatten and extract the required fields during the COPY INTO process.
D. Create an external table pointing to the source data and query it directly. Use Snowflake's JSON functions in the query to extract the relevant fields.
E. Implement a data pipeline using an external stream processing framework (e.g., Apache Kafka, AWS Kinesis) to transform and flatten the JSON data before loading it into Snowflake using Snowpipe.

Answer: E

Explanation:
Option C is the most efficient and scalable. Using an external stream processing framework allows for real-time or near real-time transformation and flattening of the JSON data before it even reaches Snowflake, offloading the processing burden. Options A and B might struggle with very large and complex JSON structures. The SnowSQL script in option A will be complex and less efficient. Transforming within a view in option B delays the transformation and can impact query performance. Option D might face performance issues as passing each record to a Python UDF can be overhead heavy. External tables (option E) are generally best for read-only access and might not be suitable for complex transformations.

NEW QUESTION # 46
Which of the following is a key step in data preparation?

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

Answer: D

NEW QUESTION # 47
When conducting diagnostic analysis, why is it essential to collect related data and demographics?

A. To focus solely on isolated anomalies
B. To overlook statistical trends in historical data
C. It assists in identifying reasons/causes of anomalies in historical data
D. To restrict analysis to recent data only

Answer: C

Explanation:
Collecting related data and demographics aids in identifying reasons/causes behind anomalies in historical data.

NEW QUESTION # 48
You are developing a data pipeline that involves loading data from multiple CSV files stored in an Amazon S3 bucket into a Snowflake table. The files have different schemas (different column names and data types), but all files contain a common column named 'record_type' that identifies the schema of the data in that file. You need to create a single Snowflake table that can store data from all the files, while ensuring data integrity and proper data typing based on the 'record_type'. Which of the following approaches is the MOST efficient and scalable method to achieve this in Snowflake?

A. Use Snowflake's VARIANT data type for a single column in the Snowflake table. Load the entire CSV file content as a JSON string into this column. Create views to extract and cast the data based on the 'record_type'.
B. Create a single external table pointing to the S3 bucket. Use a Snowflake stream on the external table to track changes. Implement a series of tasks, one for each 'record_type', that transform data from the stream and load into a single target table with appropriate datatypes.
C. Create multiple Snowflake tables, one for each 'record_type', with the corresponding schema. Use a Snowflake task that runs periodically to identify new files in the S3 bucket, determine their 'record_type', and load them into the appropriate table using a dynamic SQL query.
D. Create a single Snowflake table with a VARIANT column to store the raw data. Use a Snowpipe to load the data continuously. Implement a stored procedure that is triggered by the Snowpipe to parse the VARIANT data based on 'record_type' and insert it into correctly typed columns in the same table.
E. Create a single, wide Snowflake table with all possible columns from all CSV files, defining all columns as VARCHAR. Load all data into this table, then create views for each 'record_type' that cast the data to the correct data types.

Answer: C

Explanation:
The most efficient and scalable method involves creating multiple Snowflake tables, one for each 'record_type'. Loading data into correctly typed tables, using a task to dynamically identify files and load them into the appropriate table, leverages Snowflake's strengths. While VARIANT can store different schemas, querying and processing data in VARIANT columns are less performant than querying and processing data with correct data types. A single wide table with all VARCHAR columns requires extensive casting in views. External tables with streams can work, but creating multiple tasks for each record type becomes complex. Choosing the correct data type at load time and having well-defined schemas will minimize transformation later.

NEW QUESTION # 49
You are tasked with creating a new data model in Snowflake for a marketing analytics team. The source data is in a 3rd Normal Form (3NF) relational database. The team requires fast query performance for ad-hoc analysis and dashboards, primarily focusing on sales trends by product category, region, and customer segment. Which of the following approaches is MOST effective for transforming the 3NF data into a consumption-ready layer in Snowflake?

A. Migrate the data to Snowflake and implement a Data Vault model for long-term data management and historical tracking, then build a dimensional model on top for the marketing team.
B. Transform the data into a star schema with a fact table containing sales metrics and dimension tables for product category, region, and customer segment. Use Snowflake's clustering feature on the fact table based on date.
C. Create a series of materialized views that aggregate the data at different levels of granularity, such as daily sales by product and region.
D. Load the data into a single, wide table using a CTAS statement with all necessary columns for the marketing team's analysis.
E. Replicate the 3NF database structure directly into Snowflake and create views for the BI tool.

Answer: B

Explanation:
A star schema is optimized for Bl querying due to its denormalized structure and clear separation of facts and dimensions. Clustering the fact table on date further improves query performance for time-based trend analysis. Replicating the 3NF structure would not provide the necessary performance for ad-hoc analysis. Data Vault, while beneficial for long-term data management, adds complexity and overhead for this specific use case. A single wide table can lead to performance issues with a large number of columns. While materialized views can help, a star schema provides a more fundamental structure optimized for the analysis patterns described.

NEW QUESTION # 50
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