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Snowflake DAA-C01 Real Testing Environment - Dumps DAA-C01 PDF

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
What factors should be considered when evaluating which transformations are required in data discovery? (Select all that apply)

A. Data normalization
B. Business use cases
C. Data consistency
D. Data redundancy

Answer: A,B,C

Explanation:
Evaluating necessary transformations involves considering data consistency, normalization, and alignment with business use cases.

NEW QUESTION # 89
How do Snowsight dashboards enable effective data presentation for business use analyses?

A. Snowsight doesn't support visual data representation.
B. They enable diverse data representation for effective analyses.
C. Snowsight offers limited data representation options.
D. Snowsight dashboards rely solely on text-based representations.

Answer: B

Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.

NEW QUESTION # 90
You are working with a 'product catalog' table that contains 'product id', 'product_name', 'category', and 'price'. You notice that some product names have leading and trailing whitespace, inconsistent capitalization, and special characters. Which combination of Snowflake functions and techniques would provide the MOST comprehensive solution for cleaning the 'product_name' column to ensure consistency and accuracy in your analysis? (Select all that apply)

A. Use 'TRIM()' to remove leading and trailing whitespace.
B. Create a Snowflake UDF (User-Defined Function) that encapsulates all the cleaning steps for reuse and better code organization.
C. Use ' REPLACE(Y in a nested fashion, or regular expression functions like 'REGEXP REPLACE()' , to remove or replace special characters.
D. Use or to standardize capitalization.
E. Manually review and update each product name individually within the table using UPDATE statements.

Answer: A,B,C,D

Explanation:
Options A, B, C, and D are all valid and contribute to a comprehensive solution. 'TRIM()' removes whitespace, 'UPPER()' or 'LOWER()' standardizes capitalization, 'REPLACE()' or 'REGEXP handles special characters, and a UDF allows for reusable and organized code. Option E is highly inefficient and not scalable for large datasets. The question targets knowledge of various Snowflake cleaning functions and UDF for reusability.

NEW QUESTION # 91
You are tasked with ingesting data from a variety of external sources (JSON, CSV, Parquet) into Snowflake to build a unified customer profile. The sources contain inconsistencies in data types, null handling, and naming conventions. Your goal is to create a robust ingestion pipeline that identifies data quality issues and prepares the data for analysis. Which combination of Snowflake features and approaches would be most effective for achieving this?

A. Utilize Dynamic Tables with appropriate transformation logic defined to handle inconsistencies and maintain data quality during ingestion. Leverage Snowflake's data governance features in conjunction with Dynamic Tables for comprehensive data management
B. Use Snowpipe with schema evolution enabled and rely solely on Snowflake's automatic data type conversion. Implement data quality checks in downstream views.
C. Leverage Snowflake's data governance features with data profiling, implement data quality rules in a data catalog, and use a combination of Snowpipe and COPY INTO with data transformations within the COPY INTO statement.
D. Build a custom data ingestion application using the Snowflake Connector for Python, implementing complex data transformations and quality checks within the application before loading the data into Snowflake. Store raw data as is for future use.
E. Employ a third-party ETL tool integrated with Snowflake, configure data cleansing and transformation rules within the ETL tool, and load the transformed data into Snowflake using its optimized connectors.

Answer: A,C,D,E

Explanation:
Options B, C, D, and E represent robust approaches. B combines data governance and built-in transformations. C allows for custom logic using the Python connector. D uses a specialized ETL tool. E leverages Dynamic Tables for automated transformations and data quality maintenance. A is insufficient as it relies too heavily on automatic conversion and delays quality checks. The best approach would involve data profiling to understand the data, implementing data quality rules at the point of ingestion or through dynamic tables, and leveraging a transformation approach suited to the complexity of the transformations.

NEW QUESTION # 92
You are preparing a CSV file for ingestion into Snowflake, and you need to ensure that the data types are correctly interpreted. The CSV contains a column named 'transaction_amount' that sometimes contains values with leading zeros (e.g., '00123.45'). You want to load this data into a Snowflake table where 'transaction_amount' is defined as NUMBER(IO, 2). Without modifying the CSV file itself, how can you ensure that the leading zeros are handled correctly during the COPY INTO operation?

A. Snowflake automatically handles leading zeros in numeric fields during COPY INTO, so no special action is required.
B. During the COPY INTO operation, use the 'TRANSFORM_COLUMN' option to cast the VARCHAR column to a NUMBER(10, 2). Snowflake will implicitly handle the leading zeros during the cast.
C. Use the 'VALIDATE option in COPY INTO to identify rows with leading zeros and manually correct them in the CSV file.
D. Define the 'transaction_amount' column as VARCHAR in Snowflake, load the data, and then cast it to NUMBER(10, 2) using a transformation query, which will implicitly remove leading zeros.
E. Use the 'STRIP file format option to remove the leading zeros before loading.

Answer: B

Explanation:
Option E is the correct approach. Snowflake implicitly handles leading zeros when casting a VARCHAR column to a NUMBER type using during the COPY INTO operation. This avoids modifying the original CSV file or requiring a separate transformation step after loading. Option A is incorrect, as implicit type conversion might not always work as expected. Option B and C are incorrect because 'STRIP NULL_VALUE' is not relevant in the case of leading zeros. Option D is viable, but less efficient than handling during load time.

NEW QUESTION # 93
......

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