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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Perform Simple Data Transformations for Analysis	15–20%	- Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets
Topic 2: Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
Topic 3: Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions
Topic 4: Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis
Topic 5: Prepare and Load Data	15–20%	- Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro
Topic 6: Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Topic 7: Prepare and Present Data	10–15%	- Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q15-Q20):

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

A. Operationalizing data ensures consistent and efficient usage.
B. It restricts data updates, affecting dashboard accuracy.
C. Operationalizing data complicates dashboard management.
D. It limits the usability of reports by narrowing down access.

Answer: A

Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 16
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?

A. Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.
B. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
C. Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.
D. Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
E. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.

Answer: A,B,C,D

Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.

NEW QUESTION # 17
Your company needs to comply with GDPR regulations, and you're tasked with implementing data masking to protect sensitive customer information. Specifically, you need to mask email addresses in a 'CUSTOMERS' table during data preparation. Which of the following approaches, utilizing Snowflake's system functions and data masking capabilities, offers the MOST secure and flexible solution, while allowing authorized users to potentially unmask the data if necessary?

A. Use the 'MD5()' function to encrypt the email addresses. Authorized users can decrypt the MD5 hash using the appropriate key.
B. Create a masking policy using the function to redact portions of the email address. Apply this policy to the EMAIL_ADDRESS column in the CUSTOMERS table.
C. Create a masking policy that uses 'CASE statement based on IS_ROLE_IN_SESSION() function. When the role is Analyst role it should display original value, if not then it must display null value. Apply this policy to the EMAIL ADDRESS column in the CUSTOMERS table.
D. Create a view that uses 'REGEXP REPLACE()' function to redact portions of the email address. Grant access to the view instead of the table.
E. Use the 'SHA2()' function with a salt to hash the email addresses. Authorized users can reverse the hash using the correct salt.

Answer: C

Explanation:
Option C provides the best combination of security and flexibility. Snowflake's masking policies, combined with functions like , allow for dynamic masking based on user roles. Authorized users (e.g., those in an 'analyst' role) can see the original email address, while others see a masked version. Options A and D are not recommended as hashing/encryption functions like SHA2() and MD5() are not easily reversible and are not the intended use case for data masking policies. Option B offers masking with REGEXP_REPLACE but no mechanism to unmask with proper permissions. Option E creates a view, which is not centrally managed or scalable solution.

NEW QUESTION # 18
You are working with a large dataset of website clickstream data'. You need to perform several data transformation steps, including filtering, aggregating, and joining with other tables. You want to ensure that your data cleaning and transformation process is idempotent, meaning that running the same transformation pipeline multiple times will produce the same result, even if the input data changes slightly. Which of the following strategies contribute to building an idempotent data transformation pipeline in Snowflake? (Select all that apply)

A. Relying solely on 'INSERT INTO' statements for data loading.
B. Always truncating the target table before inserting transformed data.
C. Using temporary tables instead of permanent tables for transformation.
D. Using 'MERGE statements with appropriate matching conditions to update or insert records based on the latest data.
E. Using clones of source tables as input to the transformation pipeline, ensuring that the source data remains unchanged during the process.

Answer: B,D,E

Explanation:
Options A, B, and D are correct. Truncating the target table (A) before each run ensures a clean slate. Using 'MERGE statements (B) handles both updates and inserts, ensuring that the target table reflects the latest data state regardless of previous runs. Using clones (D) protects the original source data from unintended modifications during the transformation process, contributing to idempotency. Option C using just INSERT INTO is not idempotent as it will duplicate data on rerun. Option E, while useful for performance and cleanup, doesn't directly contribute to idempotency. The use of temporary tables alone does not guarantee idempotency if the operations performed on them are not idempotent. You might still see inconsistencies in data if the operations are not carefully designed. Truncating permanent table before adding data using MERGE is more reliable and ensures consistent result even on multiple runs.

NEW QUESTION # 19
You are analyzing sales data for a retail company. The 'sales' table contains columns 'product id' (INT), 'sale date' (DATE), and 'sale_amount' (NUMBER). You need to calculate the percentage contribution of each product's sales to the total sales on each day. You want the result to include 'sale date', 'product_id', 'sale_amount', 'daily total' , and 'percentage_contribution'. Which of the following Snowflake queries achieves this correctly?

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

Answer: A,B

Explanation:
Options A and D are correct. Option A uses a window function OVER (PARTITION BY to calculate the total sales for each day. This is efficient because it calculates the total sales for each day in a single pass. Option D uses a subquery to calculate the total sales for each day and then joins this result back to the original table. Option B calculates a single total over the entire table, not by day. Option C calculates a single total sales amount over the entire table and assigns it to every row, which is incorrect. Option E orders the sales amount by the date but doesnt correctly partition by 'sale_date' leading to cumulative sum instead.

NEW QUESTION # 20
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