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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
You are tasked with analyzing website traffic patterns using Snowflake. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE) and 'PAGE_VIEWS (NUMBER). You need to identify anomalies (unusually high or low traffic days) using the 'STDDEV POP function to calculate the standard deviation and flag days that fall outside a certain number of standard deviations from the mean. Which of the following SQL queries BEST implements this anomaly detection logic, flagging days with page views more than 2 standard deviations above the mean?

Answer: E

Explanation:
Option D correctly uses window functions to calculate the average and standard deviation across the entire dataset without grouping, then filters the rows where 'PAGE_VIEWS' are more than 2 standard deviations above the mean. Window functions (OVER ()) allow aggregate calculations without collapsing the rows, making it suitable for anomaly detection on a row-by-row basis. Option A and C incorrectly tries to use aggregate function without group by condition, and option B without CTE its creating ambiguity. Option E has logical issue.

NEW QUESTION # 53
You are tasked with loading multiple CSV files from an external stage into a Snowflake table. The files have the following naming convention: HHMlSS.csv' (e.g., 'data_20240101 120000.csv'). You need to load all the files that were created on or after '2024-01-01' into the 'target_table'. Which of the following approaches can be used to load those data?

A. Using a Snowflake task that runs daily, retrieves all the file names from the stage, filters the file names based on created date and loops through each filtered file name to load them
B. Using a single COPY INTO statement with a pattern match against METADATA$FILENAM The matching logic is done inline with the COPY INTO statement using regex.
C. Using a loop with dynamic SQL to construct COPY INTO statements for each matching file based on the date substring.
D. Using a single COPY INTO statement with a wildcard pattern in the file path and filtering in a subsequent INSERT INTO statement using a WHERE clause on the filename derived from METADATA$FILENAME.
E. Using a Snowpipe with a file notification integration, configuring the notification to trigger only for files created on or after '2024-01-01'.

Answer: B,D

Explanation:
Option B is a viable solution. COPY INTO can be combined with METADATA$FILENAME filtering to load files matching a criteria. Option E provides a better, more efficient solution that leverages the 'METADATA$FILENAME' pseudo-column to filter files directly within the 'COPY INTO' statement using regular expressions or string functions to extract and compare the date from the filename. Option A will work but is very inefficient. Option C will work, but complex to maintain. Option D is more complex to setup than option E. Snowpipe generally does not accept a single COPY command but rather an ongoing copy operation

NEW QUESTION # 54
In data presentations for business use analyses, why is identifying patterns and trends crucial?

A. Patterns and trends have minimal impact on business use analyses.
B. Recognizing patterns and trends restricts data exploration.
C. Identifying patterns and trends aids in insightful analyses.
D. It complicates data analysis, hindering decision-making.

Answer: C

Explanation:
Identifying patterns and trends aids in insightful analyses in business use scenarios.

NEW QUESTION # 55
When handling erroneous data in Snowflake, what approaches can be employed to effectively address data anomalies and ensure data integrity? (Select all that apply)

A. Performing data transformations without error handling
B. Using error tables to capture and analyze erroneous data
C. Utilizing clones for isolated error resolution
D. Dropping all erroneous records

Answer: B,C

Explanation:
Employing error tables to capture erroneous data for analysis and utilizing clones for isolated error resolution ensures effective handling of data anomalies and maintains data integrity in Snowflake.

NEW QUESTION # 56
You are loading data from a series of CSV files into Snowflake using Snowsight. The files have varying column orders and some files are missing certain columns. You need to ensure that all data is loaded into a consistent table schema, handling missing columns gracefully.
Which of the following strategies is MOST effective in Snowsight to achieve this?

A. Load the data into a staging table with a single VARIANT column. Then, use SQL with 'CASE statements and 'GET ' function to extract data from the VARIANT column into the target table with the desired schema.
B. Pre-process the CSV files before loading using a scripting language (e.g., Python) to standardize the column order and add missing columns with NULL values. Then, load the pre-processed files into Snowflake using Snowsight.
C. Define a file format with 'SKIP_HEADER = 1 ' and load all CSV files into a single table with all possible columns defined as VARCHAR. After loading, use SQL queries with and to convert the data to the appropriate data types.
D. Define a file format with 'SKIP HEADER = 1', "FIELD OPTIONALLY ENCLOSED BY = and "NULL _ IF = (", 'NULL')'. Create a single table with all columns defined and use Snowsight's 'Load Data' wizard to load the CSV files. Columns not present in a given CSV file will automatically be populated with NULL.
E. Create multiple file formats, one for each unique CSV file structure. Use Snowsight's 'Load Data' wizard to load each set of files with the corresponding file format. Use UNION ALL to combine the data from multiple tables into a single view.

Answer: D

Explanation:
Option D is the most effective strategy. By defining the file format with 'SKIP_HEADER = , FIELD_OPTIONALLY_ENCLOSED_BY ' and 'NULL_IF = (", 'NULL')', Snowflake can handle missing columns by populating them with NULL values during the load process. Creating a single table with all columns defined ensures data consistency. Option A works, but the type conversion after loading is less efficient and more error-prone. Option B requires managing multiple file formats and using UNION ALL, which can be complex. Option C using VARIANT will work, but adds extra complexity. Option E requires an external preprocessing step, which is less desirable.

NEW QUESTION # 57
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