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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Prepare and Present Data	10–15%	- Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements
Prepare and Load Data	15–20%	- External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe
Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
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 - Views, materialized views, CTEs - Handling NULLs and structuring datasets
Perform Descriptive and Diagnostic Analysis	10–15%	- Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
In Snowflake, what factors determine the effectiveness of using materialized views for query optimization?

A. Compatibility with specific BI tools only
B. Frequency of data updates and refresh requirements
C. Query result caching capabilities
D. Limitations in accessing historical data

Answer: B,C

Explanation:
Materialized views' effectiveness depends on factors like data update frequency and query result caching, impacting query optimization based on the nature of data updates and caching capabilities.

NEW QUESTION # 13
How do Materialized views differ from Regular views in the context of data analysis?

A. Regular views offer precomputed snapshots, differentiating them from Materialized views.
B. Regular views provide a persisted snapshot of data, unlike Materialized views.
C. Materialized views simplify complex data structures for ease of analysis, unlike Regular views.
D. Materialized views restrict data accessibility compared to Regular views.

Answer: A

Explanation:
Materialized views offer precomputed snapshots, differentiating them from Regular views which don't precompute data.

NEW QUESTION # 14
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?

A. Create a task that periodically runs a query that joins 'CUSTOMER DATA with both shared views (CUSTOMER ENRICHMENT from Share A and Share B), filtering based on the credit risk threshold. Insert the results into a new enriched table.
B. Create two separate views, one for each data share, and then join them based on in a final view, filtering for the credit risk threshold in the final view.
C. Create a stored procedure that iterates through the 'CUSTOMER_DATA' table, retrieves demographic and credit risk information for each customer, applies the credit risk threshold, and inserts the enriched data into a new table.
D. Create a single view that joins 'CUSTOMER_DATA' with both shared views ('CUSTOMER_ENRICHMENT from Share A and Share B) using a common table expression (CTE) to filter records from Share B to include only customers with a credit risk score above the defined threshold.
E. Replicate data from both data shares and perform enrichment and credit risk filtering on the replicated data.

Answer: D

Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.

NEW QUESTION # 15
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?

A. Employ a combination of Snowpipe for near real-time data ingestion into a raw table, and then use Snowflake's Search Optimization Service for faster queries.
B. Utilize an external stream processing engine to pre-aggregate the data into time windows (e.g., I-minute, I-hour) before ingesting into Snowflake using Snowpipe.
C. Ingest data directly into a single Snowflake table using Snowpipe with JSON data type. Create separate materialized views for real-time dashboards and historical trend analysis.
D. Implement Snowpipe for initial data ingestion, complemented by Snowflake's clustering feature based on timestamp to optimize historical analysis queries. And employ a stream processing engine to perform time window pre-aggregation.
E. Use Snowpipe to ingest data into a raw landing table, and then use Snowflake tasks to transform and load the data into separate tables optimized for real-time dashboards and historical analysis.

Answer: D,E

Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.

NEW QUESTION # 16
What complexities might arise when identifying and resolving data import errors in Snowflake?
(Select all that apply)

A. Handling only specific error types
B. Analyzing error logs for resolution
C. Resolving data inconsistencies
D. Identifying error sources

Answer: B,C,D

Explanation:
Identifying and resolving data import errors involves challenges related to identifying error sources, resolving inconsistencies, and analyzing logs for resolution, which might be complex depending on the nature of the errors.

NEW QUESTION # 17
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