DAA-C01 Exam Guide - DAA-C01 Test Questions & DAA-C01 Exam Torrent

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Topic 2: Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas
Topic 3: Data Transformation and Analysis	- Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics - SQL-based transformations 1. Joins, aggregations, window functions 2. Semi-structured data (VARIANT, JSON, XML)
Topic 4: Security, Governance, and Data Sharing	- Data sharing and governance 1. Data masking and policies 2. Secure data sharing - Access control and security 1. Role-based access control (RBAC) 2. Authentication and encryption concepts
Topic 5: Data Loading and Unloading	- Data export 1. UNLOAD and external stages - Data ingestion methods 1. Continuous ingestion and Snowpipe concepts 2. COPY INTO and bulk loading

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

NEW QUESTION # 12
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?

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

Answer: A

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

NEW QUESTION # 13
You've identified a 'Filter' operation in a Snowflake query execution plan that is consuming a significant amount of time. The filter predicate involves a UDF (User-Defined Function) called 'calculate_score(columnl, column2)'. The UDF is written in Python. Analyzing the plan, you observe a high number of rows being processed by this filter. How can you optimize this scenario for faster query execution?

A. Increase the warehouse size to provide more resources for UDF execution.
B. Create a materialized view that pre-calculates the score using the 'calculate_score' UDF and stores the results. The query should then filter on the materialized view.
C. Replace the UDF with a regular expression to mimic the calculation to increase performance
D. Implement caching within the UDF to store previously calculated scores and reuse them for identical inputs.
E. Rewrite the UDF in SQL instead of Python to leverage Snowflake's native execution engine.

Answer: B,E

Explanation:
Options A and C provide significant performance improvements. A moves the computation to Snowflake's engine, likely improving speed. C pre-calculates the score, avoiding repeated UDF calls. While increasing warehouse size (B) might help, it doesn't address the fundamental inefficiency of the UDF. Caching within the UDF (E) can improve performance if there are repeated calls with the same inputs, but a SQL based code or materialized view is better solution in this case. Regular Expressions don't have computational power for any complex calculations.

NEW QUESTION # 14
A healthcare provider is investigating patient readmission rates within 30 days of discharge. They suspect a correlation between patient demographics (age, gender, location) and readmission. You have the following tables: 'PATIENTS': 'patient_id', 'age', 'gender' , 'zip_code' 'ADMISSIONS': 'admission_id' , 'patient_id' , 'admission_date', 'discharge_date' Which of the following approaches would be MOST effective to identify patient demographics significantly correlated with higher readmission rates within 30 days? (Select TWO)

A. Create a Snowflake user-defined function (UDF) in Python to perform a complex machine learning model directly on the data to predict readmissions based on demographics, without any initial exploratory data analysis.
B. Implement a cohort analysis to track patient readmission rates over time for different demographic segments and visualize the trends using Snowflake's data visualization capabilities (if integrated) or export the data to a BI tool.
C. Use correlation coefficients (e.g., Pearson, Spearman) to directly measure the linear association between demographics and the binary readmission outcome (1 -readmitted, 0=not readmitted).
D. Calculate the overall readmission rate and compare it to the readmission rates for different demographic groups using Chi-Square tests or similar statistical methods to assess statistical significance.
E. Develop a complex SQL query to directly identify and list all patients who have been readmitted more than twice in the last year, irrespective of their demographics.

Answer: B,D

Explanation:
Options A and D are the most effective. A suggests using statistical tests (Chi-square) which can identify statistically significant differences in readmission rates across demographic groups. D proposes cohort analysis, enabling the tracking and visualization of readmission trends for different demographics over time, allowing for the identification of segments with consistently high readmission rates. Option B is not optimal as it jumps directly into complex modeling without initial EDA. Option C, while valid, doesn't handle categorical variables (gender, zip_code) well and might miss non-linear relationships. Option E, identifying high-frequency readmitters, is helpful but doesn't directly address the relationship with demographics.

NEW QUESTION # 15
You are building a Snowsight dashboard to monitor the performance of various SQL queries. You have a table 'QUERY HISTORY with columns 'QUERY ID', 'START TIME, 'END TIME, 'USER NAME, 'DATABASE NAME, and 'EXECUTION_TIME (in seconds). You want to create a bar chart that shows the average execution time for each user, but only for queries executed against a specific database (e.g., 'SALES DB') within the last week. Furthermore, you need to allow users to filter the data by username via a Snowsight dashboard variable. What is the most efficient SQL query and Snowsight configuration to achieve this?

A. Option D
B. Option E
C. Option B
D. Option C
E. Option A

Answer: C

Explanation:
Option B provides the most efficient solution by using a Snowsight dashboard variable ('USERNAME) with a dropdown populated from the distinct usernames in the 'QUERY HISTORY table. This allows users to easily filter the data by selecting a username from the dropdown, and the SQL query directly incorporates the variable using '$USERNAME in the WHERE clause for optimal performance. Option A lacks the dynamic filtering capability. Option C will not work as 'HAVING' clause should use aggregate functions, and even if it worked is not efficient. Option D would be difficult to maintain and less performant. Option E introduces unnecessary complexity with a stored procedure for this specific requirement.

NEW QUESTION # 16
How do materialized views differ from regular views in terms of data storage and computation?

A. Materialized views simplify complex data structures for better computation.
B. Regular views provide precomputed snapshots, unlike materialized views.
C. Regular views provide precomputed snapshots for improved query performance.
D. Materialized views restrict data storage for better computation.

Answer: B

Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.

NEW QUESTION # 17
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