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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?

A. Limited external data sources
B. Data format compatibility issues
C. Inability to access marketplace data
D. High data redundancy

Answer: B

Explanation:
Correlating external datasets might face challenges due to data format compatibility issues between different sources.

NEW QUESTION # 61
You have a table named 'SALES DATA' containing daily sales records. You need to identify and handle outliers in the 'SALES AMOUNT' column. Specifically, you want to replace any 'SALES AMOUNT values that fall outside of three standard deviations from the mean with the median 'SALES AMOUNT. What is the most efficient way to achieve this data transformation in Snowflake?

A. Calculate mean and standard deviation using separate queries, then use an UPDATE statement with a WHERE clause to identify and replace outliers.
B. Export the data to an external system, perform the outlier detection and replacement there, and then load the cleaned data back into Snowflake.
C. Clone the table, calculate the mean, stddev and median on the original table, and use it in CASE statement, and create new sales amount column for identifying outliers and insert into the cloned table.
D. Use a single 'CREATE OR REPLACE TABLE AS SELECT (CTAS) statement with window functions and a 'CASE expression to calculate the mean, standard deviation, and median, and replace outliers in the same query.
E. Create a stored procedure to iterate through each row and check for outliers, updating the 'SALES_AMOUNT accordingly.

Answer: D

Explanation:
Option C is the most efficient approach. Using a CTAS statement with window functions allows you to calculate the mean, standard deviation, and median in a single pass through the data. The 'CASE expression enables you to identify and replace outliers inline, avoiding multiple queries or iterative procedures. Options A involves multiple queries, Option B is inefficient due to row-by-row processing in a stored procedure, and Option D involves unnecessary data transfer to an external system. Option E is similar to C but uses a clone unnecessarily adding complexity and cost. Clones are useful for isolating changes not necessarily part of the transformation itself. The CTAS operation provides similar isolation within a single statement.

NEW QUESTION # 62
What actions are involved in data discovery to identify necessary elements from available datasets in Snowflake? (Select all that apply)

A. Identifying missing data fields
B. Running SQL queries on tables
C. Performing data mining techniques
D. Evaluating necessary transformations

Answer: B,D

Explanation:
Data discovery in Snowflake involves querying tables and evaluating required transformations for dataset refinement.

NEW QUESTION # 63
Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?

A. Create a Snowflake table with clearly defined columns matching the Parquet schema. Use 'COPY INTO' with a transformation to load the data. Update the table definition whenever the Parquet schema changes.
B. Create an external table with 'AUTO_REFRESH = TRUE and specify a schema on read using 'FILE_FORMAT = (TYPE = PARQUET, STRIP OUTER ARRAY = TRUE, = TRUE)'. Query the external table directly.
C. Load the Parquet data into a VARIANT column in a Snowflake table using 'COPY INTO'. Create a view on top of this table, using JSON EXTRACT_PATH_TEXT to extract specific fields into separate columns.
D. Create an external table pointing to the Parquet files. Define a materialized view on top of the external table, extracting the required fields. Refresh the materialized view periodically.
E. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly using to access nested fields. Create a view on top of the external table to simplify queries.

Answer: D

Explanation:
Materialized Views offer the best performance as they pre-compute and store the results, leveraging Snowflake's caching. They also adapt to schema changes in underlying Parquet files (within limits). External tables alone can be slow because of on-the-fly processing. Loading into VARIANT loses the advantage of Parquet's columnar structure. Predefined columns are rigid and don't handle schema evolution well. Creating standard views on external tables also does not provide the pre-computed benefits of the materialized views.

NEW QUESTION # 64
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?

A. Visualizations don't impact data comprehension or analysis significantly.
B. They enhance data comprehension, aiding effective analysis.
C. Presenting data visually increases complexity in analysis.
D. Visualizations limit data exploration and analysis capabilities.

Answer: B

Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.

NEW QUESTION # 65
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