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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
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. Using 'MERGE statements with appropriate matching conditions to update or insert records based on the latest data.
B. Always truncating the target table before inserting transformed data.
C. Relying solely on 'INSERT INTO' statements for data loading.
D. Using temporary tables instead of permanent tables for transformation.
E. Using clones of source tables as input to the transformation pipeline, ensuring that the source data remains unchanged during the process.

Answer: A,B,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 # 22
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. 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.
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. 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.
D. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.
E. 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.

Answer: A,B,C,E

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 # 23
You have Parquet files containing customer order data in an external stage. You need to create a secure view in Snowflake that filters the data based on user roles. Only users with the 'SALES REP' role should see orders from their assigned region, while 'MANAGER' roles should see all data'. Assuming you have set up appropriate role hierarchies, how would you implement this using row-level security policies with Parquet external tables?

A. Create a standard view querying the external table. Create a row access policy that filters the data based on the user role and the region. Apply the policy to the external table. Delegate the ownership to 'ACCOUNTADMIN'.
B. Create a secure view that queries the external table. Then, define a row access policy that filters the data based on the user role and the region, and apply the policy to the secure view. The policy should use to determine the appropriate filtering criteria. Delegate the ownership to 'ACCOUNTADMIN'.
C. Create an external table with a schema defined according to the Parquet files. Define a masking policy that hides the sensitive information for users without proper roles. Create a standard view on top of the external table. Apply the masking policy to a column inside view.
D. Create a secure view that queries the Parquet files directly using 'JSON EXTRACT PATH TEXT to access the region information. Apply a JOIN operation with a role table to filter the data based on the user's role.
E. Create a secure view that directly queries the Parquet files using to access the region information. Apply a WHERE clause in the view definition using to filter based on the user's role. No RLS policy needed.

Answer: B

Explanation:
Row Access Policies (RAP) are designed for Row Level Security, and they are applied to tables or views. Since the data is in an external table accessed through a view, the policy should be attached to the secure view. The ' function is used to determine the user's current role within the policy's logic. Secure Views protect the underlying data and policy logic from unauthorized access. Delegating ownership to 'ACCOUNTADMIW is not strictly required but a good security practice. Masking policies aren't the correct choice here as it is used to hide data from the user.

NEW QUESTION # 24
Identifying demographics and relationships in a dataset is crucial for:

A. Database normalization
B. Data cleaning
C. Data modeling
D. Error handling

Answer: C

NEW QUESTION # 25
A data analyst needs to ingest data from various sources into Snowflake, cleanse it, and load it into target tables. Which of the following actions are MOST crucial for ensuring data quality and consistency during the ingestion and preparation phases?

A. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
B. Using Snowflake's data masking policies to protect sensitive data during ingestion.
C. Storing all data in a single large table to avoid data silos.
D. Enforcing data type constraints on Snowflake table columns.
E. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.

Answer: A,D,E

Explanation:
Options A, B, and E are most crucial for data quality and consistency. Enforcing data type constraints (A) ensures that only data of the correct type is loaded into the tables. Implementing data validation checks (B) using UDFs or stored procedures allows for detecting and handling invalid data, preventing it from corrupting the data warehouse. Data profiling (E) helps to understand data quality issues before the load and guides data cleansing efforts. Storing all data in a single table (C) is generally not recommended as it can lead to performance issues and make it harder to manage data. While data masking (D) is important for data security, it is not directly related to data quality and consistency during ingestion and preparation. Data dictionaries promote data quality and usability.

NEW QUESTION # 26
......

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