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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and Load Data	15–20%	- Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro
Topic 2: Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Topic 3: Perform Descriptive and Diagnostic Analysis	10–15%	- Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis
Topic 4: Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements
Topic 5: Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting
Topic 6: Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Topic 7: Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
How does Snowflake's support for materialized views contribute to query optimization and data analysis?

A. Materialized views increase data storage requirements only
B. Materialized views improve query performance by pre-computing results
C. Materialized views limit query optimization possibilities
D. Materialized views restrict data access across warehouses

Answer: B

Explanation:
Materialized views in Snowflake enhance query performance by pre-computing and storing query results, optimizing data analysis by providing faster access to aggregated data.

NEW QUESTION # 57
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?

A. Data shares only work with Snowflake-provided datasets.
B. Data shares facilitate secure data exchange between parties.
C. They limit the types of data that can be joined.
D. They restrict access to external data.

Answer: B

Explanation:
Data shares enable secure data exchange, allowing joining external data with existing datasets.

NEW QUESTION # 58
While reviewing the query profile for a complex data transformation pipeline, you notice a significant amount of time spent in the 'Join' operation between two large tables, 'transactions' and 'customers'. The join is performed on the 'customer _ id' column. Which of the following are potential strategies to optimize the join performance?

A. Rewrite the query to use a 'LATERAL FLATTEN' function instead of a 'JOIN'.
B. Broadcasting the smaller table to all compute nodes.
C. Ensure that the 'customer_id' column has identical data types in both tables.
D. Cluster both tables on the 'customer id' column.
E. Increase the size of the virtual warehouse.

Answer: C,D

Explanation:
Mismatched data types can cause implicit type conversions, which can significantly degrade join performance. Clustering both tables on the join key ('customer_id') will ensure that rows with the same customer ID are located closer together on disk, reducing 1/0 and improving join efficiency. Broadcasting the smaller table is not directly controllable by the user in Snowflake and is handled automatically by the optimizer. 'LATERAL FLATTEN' is not a direct replacement for a join. Increasing the virtual warehouse size might improve overall processing time but doesn't address the specific inefficiency of the join operation.

NEW QUESTION # 59
You are tasked with building a report to analyze customer churn. The data includes customer demographics, purchase history, website activity, and support interactions. You want to provide interactive filtering capabilities within the report, allowing users to explore the data based on various criteria'. Which Snowflake feature(s) offer the most effective way to implement interactive filtering within your reporting solution without directly exposing underlying tables?

A. Creating static reports using a scheduling tool to generate PDFs.
B. Creating secure views and stored procedures, then implementing parameterized queries within the reporting tool that call the stored procedures with user-defined parameters.
C. Granting direct read access to all the underlying tables to the reporting tool's service account.
D. Using Snowflake's external functions to call a custom API that handles filtering logic.
E. Using Snowflake's Data Marketplace to directly share the raw tables with the reporting tool.

Answer: B

Explanation:
Secure views and stored procedures (option B) provide a controlled interface for accessing the data. Secure Views protect the underlying schema. Stored procedures encapsulate the filtering logic and can validate the provided parameters. This is crucial for both security and data governance. Other options are either insecure (A, D) or don't provide interactive filtering (E). While external functions could potentially work, they add unnecessary complexity compared to stored procedures.

NEW QUESTION # 60
You have two Snowflake tables: 'transactions' (containing transaction details with columns 'transaction id', 'customer id', 'amount' , 'transaction_date') and 'customer_demographicS (containing customer demographic information with columns 'customer_id', 'age' , gender' , 'location'). You need to enrich the 'transactions' table with customer demographics to analyze transaction patterns based on customer segments. What is the most efficient and scalable way to achieve this data enrichment in Snowflake, considering the 'transactions' table contains billions of rows?

A. Using Snowflake's materialized views to pre-compute the enriched data and refresh it periodically.
B. Using a standard INNER JOIN between the 'transactions' and 'customer_demographics' tables in a CREATE TABLE AS SELECT (CTAS) statement.
C. Using Snowflake Streams and Tasks to incrementally enrich the 'transactions' table as new data arrives.
D. Using a series of correlated subqueries to fetch the demographic information for each transaction record.
E. Export the tables to a data lake, perform the join using a Spark cluster, and then import the enriched data back into Snowflake.

Answer: A,B,C

Explanation:
Options A, C, and D are efficient approaches. Option A (CTAS with INNER JOIN) is often the simplest and most performant for a one- time enrichment, leveraging Snowflake's query optimization. Option C (Materialized Views) provides pre-computed enriched data, ideal for frequent queries on the enriched dataset. Option D (Streams and Tasks) enables incremental enrichment, suitable for real-time or near real-time data updates. Option B (correlated subqueries) is generally inefficient and should be avoided with large datasets. Option E introduces unnecessary complexity by exporting data to a data lake.

NEW QUESTION # 61
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