DAA-C01 Snowflake Certified SnowPro Advanced - Data Analyst Exam Free Dumps - Free Latest Dumps for IT Certification Exams

DAA-C01 Snowflake Certified SnowPro Advanced – Data Analyst Exam Free Dumps

dumps2024-12-30T09:17:25+08:00

DAA-C01 exam is one for SnowPro Advanced: Data Analyst certification, which will test advanced knowledge and skills used to apply comprehensive data analysis principles using Snowflake and its components. There are 65 Multiple Select, Multiple Choice questions in real DAA-C01 exam, and you have 115 minutes to finish the test. The exam language is English. The required passing score for DAA-C01 exam is 750. We have cracked the latest SnowPro Advanced: Data Analyst DAA-C01 exam dumps, which can ensure you pass the test easily. Free SnowPro Advanced: Data Analyst DAA-C01 exam dumps are available below.

Page 1 of 6

1. Which aspects are important when making predictions based on data for forecasting purposes? (Select all that apply)

Incorporating statistical methods for accurate predictions

Using only basic arithmetic functions for forecasting

Relying solely on historical data without considering external factors

Considering trends and anomalies in historical data

2. When cleaning data, what role does using clones play in specific use-cases?

Clones help preserve original data for audit purposes only

Clones slow down the data cleaning process significantly

Clones aid in isolating and resolving data anomalies

Clones are unnecessary for data cleaning tasks

3. In what way can regular views be advantageous in data analysis?

Regular views simplify complex data structures for ease of analysis.

Regular views can only be utilized in combination with UDFs.

Regular views don’t impact query performance significantly.

They restrict data access, improving security but hindering analysis.

4. When performing a descriptive analysis using Snowsight dashboards, how do they assist in summarizing large data sets?

Snowsight dashboards provide visual representations aiding in quick comprehension.

Snowsight dashboards limit visualization options for large data sets.

They offer detailed textual summaries instead of visual representations.

They cannot handle large data sets efficiently for summarization.

5. How does performing data discovery through querying tables in Snowflake aid in data preparation?

It limits data selection options.

Data discovery enhances data transformation understanding.

It simplifies data transformation tasks.

Querying tables doesn't impact data preparation.

6. How can stored procedures be beneficial in data analysis using SQL?

Stored procedures are limited to read-only operations.

Stored procedures can't be used in conjunction with UDFs.

They allow execution of repetitive tasks, enhancing data analysis efficiency.

Stored procedures cannot handle large data sets effectively.

7. How can User-Defined Functions (UDFs) be utilized in SQL for data analysis?

UDFs allow custom-defined operations on data, extending SQL functionalities.

UDFs are limited to basic arithmetic operations.

UDFs are exclusively used for database administration tasks.

UDFs can only be applied to structured query optimization.

8. How does leveraging partition pruning enhance query performance in Snowflake?

Limits data access for specific user roles

Reduces metadata storage requirements

Speeds up data loading processes significantly

Optimizes query planning by eliminating unnecessary partitions

9. When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?

Subscriptions and updates ensure timely information delivery.

They enhance dashboard usability without impacting data updates.

Subscriptions and updates don’t impact dashboard management.

Managing subscriptions and updates complicates dashboard usage.

10. Which statement accurately describes the usage of materialized views in data analysis?

They offer a precomputed, persisted snapshot of data, improving query performance.

Materialized views are restricted to storing small subsets of data.

Materialized views are only accessible through stored procedures.

Materialized views update in real-time, reflecting instantaneous changes in the database.

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11. When customizing data presentations in dashboards using filtering and editing techniques, what advantages do these methods offer? (Select all that apply)

Improved data relevancy

Enhanced data accuracy

Simplified data representation

Limited data exploration

12. When selecting data for building dashboards, which factors should be considered to ensure relevance and usability? (Select all that apply)

Evaluating data based on business requirements

Filtering data based on irrelevant attributes

Ignoring data complexities for simplicity in visualization

Including all available data for comprehensive visualization

13. In data modeling for BI requirements, when is it preferable to use a flattened data set instead of a data model?

For complex data analysis needs

For scenarios necessitating extensive data transformations

For situations requiring high data normalization

For quick and simple data exploration

14. In data presentations for business use analyses, what significance do identifying patterns and trends hold?

It complicates data analysis, hindering decision-making.

Recognizing patterns and trends limits data exploration.

Patterns and trends don't impact business use analyses significantly.

Identifying patterns and trends aids in insightful analyses.

15. What role does operationalizing data play in maintaining reports and dashboards for business requirements?

It limits the usability of reports by narrowing down access.

Operationalizing data ensures consistent and efficient usage.

Operationalizing data complicates dashboard management.

It restricts data updates, affecting dashboard accuracy.

16. In Snowsight, what is the significance of creating diverse chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?

It limits data presentation options for complex datasets.

Different chart types offer varied data representation for better analysis.

It restricts users to specific chart types for simplicity.

Snowsight doesn’t support multiple chart types for visualization.

17. When handling CSV, JSON, and Parquet data types for consumption, what advantages do Parquet files typically offer over the others?

JSON files offer more flexibility in schema changes

CSV files are more efficient in handling nested data structures

Parquet files provide better compression and query performance

Parquet files are not suitable for large datasets

18. How do Snowsight dashboards facilitate the presentation of data for business use analyses?

Snowsight dashboards are exclusively text-based, limiting analyses.

Snowsight limits data representation options, hindering analyses.

They enable diverse data representation for effective analyses.

Snowsight doesn't support visual data representation.

19. When loading data into Snowflake using Snowsight, what functionalities does this method offer? (Select all that apply)

Streamlined data import from external/internal stages

Batch data loading

Real-time data loading

Support for only structured data formats

20. What actions are involved in data discovery to identify necessary elements from available datasets in Snowflake? (Select all that apply)

Running SQL queries on tables

Evaluating necessary transformations

Performing data mining techniques

Identifying missing data fields

Page 3 of 6

21. How do secure views enhance data analysis practices?

They improve query performance but don't impact data security.

Secure views limit access to data, hindering analysis.

Secure views provide enhanced data security while enabling selective data access.

Secure views prevent the creation of materialized views.

22. When employing different modeling techniques for the consumption layer in Snowflake (e.g., dimensional, Data Vault), what factors influence the choice between these techniques?

Preference for optimized query performance

Data consistency requirements

Complexity of data transformations

Availability of specific database object types

23. How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?

Visualizations don't impact data comprehension or analysis significantly.

Presenting data visually increases complexity in analysis.

Visualizations limit data exploration and analysis capabilities.

They enhance data comprehension, aiding effective analysis.

24. When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?

Repeatable tasks hinder data updates in dashboards.

Automated tasks reduce manual efforts, ensuring consistency.

They ensure inconsistency in reports and dashboards.

Automated tasks increase the complexity of dashboard management.

25. Identify the correct action involved in performing an exploratory ad-hoc analysis.

Focusing on established trends without investigating anomalies

Relying solely on predefined queries without exploration

Utilizing ad-hoc queries to examine patterns and anomalies

Analyzing only a small fraction of the available data

26. How does incorporating visualizations in reports and dashboards aid in presenting data for business use analyses?

Visualizations complicate data representation, hindering analysis.

Presenting data visually doesn’t impact business use analyses.

It limits data presentation to textual formats only.

Visualizations enhance data comprehension for effective analysis.

27. When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?

Data shares only work with Snowflake-provided datasets.

Data shares facilitate secure data exchange between parties.

They restrict access to external data.

They limit the types of data that can be joined.

28. In Snowflake, how does Time Travel feature assist in data retrieval and analysis?

Limits data access for specific user roles

Accelerates query performance significantly

Enables querying data as of a specific point in time

Provides real-time data updates

29. How do Materialized views differ from Regular views in the context of data analysis?

Regular views offer precomputed snapshots, differentiating them from Materialized views.

Materialized views restrict data accessibility compared to Regular views.

Materialized views simplify complex data structures for ease of analysis, unlike Regular views.

Regular views provide a persisted snapshot of data, unlike Materialized views.

30. Which statistical method is commonly used in forecasting based on historical data?

Regression analysis

Simple data aggregation

Data normalization

Inferential statistics

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31. When performing forecasting using statistics and built-in functions, what role do these functions play?

They solely aid in data visualization without impacting the forecasting process.

They restrict forecasting to predefined models without flexibility.

They enable the creation of custom forecasting models as needed.

These functions only apply to limited data types for forecasting.

32. What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)

Merging data from multiple tables

Updating existing data

Deleting data entirely

Inserting new data

33. When connecting BI tools to Snowflake for dashboard creation, what factors need to be considered for seamless integration? (Select all that apply)

Network latency between BI tool and Snowflake

Data encryption requirements

Availability of a Snowflake account only

Compatibility of BI tool with Snowflake

34. When optimizing query performance in Snowflake, what benefits does result caching provide?

Improves schema changes management

Restricts query optimization

Limits data access for specific user roles

Speeds up query execution by storing intermediate results

35. When utilizing geospatial functions in Snowflake, what functionalities do these functions offer? (Select all that apply)

Geometric calculations

Location-based data analysis

Data encryption

Spatial indexing

36. Which actions are pertinent in identifying demographics and relationships during diagnostic analysis? (Select all that apply)

Analyzing statistical trends

Collecting related data

Ignoring data relationships for focused analysis

Examining anomalies in isolation

37. What steps are typically involved in troubleshooting query performance issues in Snowflake? (Select all that apply)

Analyzing system hardware for faults

Modifying warehouse configurations

Reviewing query history and usage logs

Examining Query Profile attributes

38. When working with Snowsight dashboards to summarize large data sets, what key advantage do they offer in exploratory analyses?

Snowsight dashboards facilitate quick, visual comprehension of complex data.

They only support basic data summarization.

They are limited to presenting static data sets.

Snowsight dashboards can't handle large data sets efficiently.

39. How do exploratory ad-hoc analyses differ from routine analysis?

They involve querying known patterns without exploring further.

Ad-hoc analyses deviate from established routines, exploring patterns and anomalies in data.

Ad-hoc analyses focus on anomalies and established trends.

Ad-hoc analyses rely heavily on predefined queries.

40. When maintaining reports and dashboards, why is it crucial to configure subscriptions and updates?

They complicate dashboard management without any added benefits.

They limit data accessibility for effective dashboard usage.

Subscriptions and updates ensure timely information delivery.

Configuring these features hampers dashboard usability.

Page 5 of 6

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

Using randomization techniques

Employing time travel for data retrieval

Identifying and handling data anomalies

Leveraging materialized views for aggregations

42. How do table functions differ from other Snowflake functions?

They perform mathematical computations exclusively.

Table functions modify existing table structures.

They only operate on entire tables.

Table functions return tables as results.

43. How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

Dynamic Data Masking doesn't affect data visibility in dashboards.

Both policies restrict data visibility for better security.

They improve data visibility for all users without restrictions.

Row access policies limit data visibility based on user privileges.

44. How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?

Both policies restrict data visibility for better security.

Dynamic Data Masking doesn't impact dashboard creation or maintenance.

Row access policies limit data visibility based on user privileges.

They enhance data visibility without any restrictions.

45. When selecting and implementing an effective data model, what considerations are crucial for ensuring its suitability for BI requirements? (Select all that apply)

Scalability and flexibility

Conformity to specific database standards only

Extensive data denormalization

Performance and ease of maintenance

46. What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)

Data redundancy requirements

Speed of data retrieval

Frequency of data analysis

Available storage capacity

47. What is the primary benefit of using User-Defined Functions (UDFs) in SQL for data analysis?

They enable customization and extension of SQL functionalities for specific data operations.

UDFs restrict data analysis to predefined operations.

UDFs can only be used with Materialized views.

UDFs hinder query optimization.

48. When working with semi-structured data in Snowflake, how do built-in functions for traversing, flattening, and nesting aid in data manipulation?

They only work with specific file formats

They facilitate handling complex and nested data structures

They limit data transformation possibilities

They restrict data access for user roles

49. How does operationalizing data contribute to maintaining reports and dashboards for business requirements?

It limits data accessibility for effective dashboard usage.

Operationalizing data complicates dashboard sharing.

It restricts data updates, affecting dashboard accuracy.

Operationalizing data ensures consistent and efficient usage.

50. How do logging and monitoring solutions contribute to data processing solutions? (Select all that apply)

Respond to processing failures promptly

Automate data processing effectively

Ensure real-time data processing

Provide insights into processing status

Page 6 of 6

51. When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)

Volume of data

Business requirements

Data collection tool limitations

Data source availability

52. How do diverse chart types (e.g., bar charts, scatter plots, heat grids) contribute to effective data presentation and visualization in reports and dashboards?

Different chart types offer varied data representation for better analysis.

Charts don't impact data visualization in reports or dashboards.

Diverse chart types restrict data exploration in reports and dashboards.

They limit data representation options for simplicity.

53. What types of Snowflake functions are available for data analysis and manipulation? (Select all that apply)

Scalar functions

System functions

Aggregate functions

Complex functions

54. Which action aids in performing a diagnostic analysis on historical data to identify reasons/causes of anomalies?

Collecting related data and demographics

Focusing on isolated data points

Ignoring statistical trends in historical data

Analyzing data solely from the past month

55. Why are Stored Procedures valuable in data analysis using SQL?

They are exclusively used for one-time data operations.

They restrict the execution of repetitive tasks, limiting efficiency.

Stored Procedures enable custom and repeated data operations, enhancing efficiency.

Stored Procedures solely facilitate data visualization.

56. How does the utilization of Regular views differ from Materialized views in data analysis?

Materialized views provide a persisted snapshot, unlike Regular views.

Materialized views offer limited data accessibility compared to Regular views.

Regular views are exclusively used for exploratory analyses.

Regular views offer better query performance compared to Materialized views.

57. What is the primary benefit of connecting BI tools to Snowflake for dashboard creation?

Simplified data access for all users

Improved data security in dashboards

BI tools restrict dashboard customization

Seamless integration and data visualization

58. How do Stored Procedures contribute to the efficiency of data analysis using SQL?

Stored Procedures enable the execution of repetitive tasks, enhancing efficiency.

They solely facilitate basic arithmetic operations.

They limit data accessibility, hindering analysis.

Stored Procedures can't be used in conjunction with User-Defined Functions (UDFs).

59. How does understanding and analyzing the query execution plan contribute to query optimization in Snowflake?

Restricts access to query results

Facilitates identifying hardware limitations

Helps in generating real-time data updates

Aids in understanding query processing steps and bottlenecks

DAA-C01 Snowflake Certified SnowPro Advanced – Data Analyst Exam Free Dumps

DAA-C01 Snowflake Certified SnowPro Advanced – Data Analyst Exam Free Dumps

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