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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design - Transform data using SQL 1. Window functions 2. QUALIFY clauses 3. PIVOT/UNPIVOT operations 4. Common Table Expressions (CTEs)
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Time-series analysis 3. Snowflake-specific analytical features
Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Power BI integration 2. Other partner visualization tools 3. Tableau integration - Create dashboards 1. Present analytical results 2. Snowsight dashboards
Data Ingestion and Data Preparation	15%-20%	- Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Perform data discovery to identify what is needed from available datasets 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 2. Evaluate required transformations (table joins, set operations, ASOF JOINS) 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Determine the level of data granularity required 5. Identify elements required for business goals using BI reports or SQL analysis - Use a collection system to retrieve data 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 2. Retrieve data from unstructured sources 3. Retrieve data from structured sources (CSV) 4. Synthetic Data Generation - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Find external data sets that correlate with available data 3. Create tables and views - Implement data processing solutions 1. Use logging and monitoring solutions (auditing, data lineage) 2. Cleanse, conform, and enrich data 3. Automate and implement data pipelines (scheduling) 4. Respond to processing failures - Use best practice considerations relating to data integrity structures 1. Define primary keys for tables 2. Perform table joins between parent/child tables 3. Implement constraints

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
Consider the following Snowflake table schema and data: 'CREATE TABLE products (product_id INTEGER, product_name VARCHAR, properties VARIANT);' Data: 'INSERT INTO products VALUES (1, 'Laptop', "silver", "storage": "512GB", "price": 1200.00}'));' 'INSERT INTO products VALUES (2, 'Mouse', "wireless", "dpi": 1600, "price": 25.00}'));' 'INSERT INTO products VALUES (3, 'Keyboard', PARSE JSON('{"layout": "US", "backlit": true, "price": Which of the following SQL queries will return the 'product_name' and 'price' for all products where the 'price' is greater than 50, ensuring that the 'price' is treated as a numeric value for comparison? Select all that apply

Answer: A,C

Explanation:
Options B and E are correct. Option B explicitly casts 'properties:price' to a 'NUMBER data type before the comparison, ensuring that the comparison is performed numerically. Option E casts 'properties:price' to a data type and uses TRY_TO_NUMBER to handle potential errors gracefully. Option A is incorrect because Snowflake treats the value extracted from the VARIANT as a string and the string comparison will lead to incorrect results. Options C and D don't work without casting to VARCHAR

NEW QUESTION # 33
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

A. Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.
B. Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
C. Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
D. Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from
E. Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.

Answer: C

Explanation:
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.

NEW QUESTION # 34
A company receives daily CSV files containing customer order data'. Each file contains a header row and is compressed using GZIP.
The files are landed in an AWS S3 bucket. The company wants to automate the data ingestion into a Snowflake table named 'orders table'. The requirements are: 1. Automated ingestion: New files should be automatically ingested as they arrive in the S3 bucket. 2. Data validation: Records with invalid dates or missing product IDs should be rejected and logged for review. 3. Data transformation: The column (string format 'YYYY-MM-DD') needs to be converted to a DATE data type, and a new column 'order _ year' needs to be derived from the 'order_date'. Which combination of Snowflake features and configurations provides the MOST efficient and reliable solution to meet these requirements?

A. Create a Snowpipe that points to the S3 bucket. Use a COPY INTO statement with 'VALIDATE(O)' and a BEFORE trigger to invoke a stored procedure that validates the data against a set of rules. Use a stored procedure to transform the data into 'orders_table' .
B. Create an external table pointing to the S3 bucket. Use a stream on the external table to track changes and a task to periodically move the new data into the 'orders_table' while performing the necessary transformations and validations. This could also be achieved using Dynamic Tables.
C. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that performs the date conversion using TO DATE() and extracts the order year using YEAR(). Configure the COPY INTO statement with "ON_ERROR = 'CONTINUE" and a validation table to log rejected records.
D. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that includes 'ON ERROR = 'SKIP_FILE". Use a downstream task to periodically validate and transform the data in the 'orders_table'.
E. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that utilizes a user-defined function (UDF) written in Python to perform complex data validation and transformation before loading the data into the 'orders_table'. Set 'ON_ERROR = 'SKIP_FILEP to avoid loading erroneous data.

Answer: B,C

Explanation:
Options B and C offer the best combination of features to address the requirements effectively. Option B leverages Snowpipe's COPY INTO statement to directly convert the date, calculate order year, and handle errors by continuing the load and logging invalid records into a validation table. This maximizes efficiency and ensures that valid data is ingested quickly. ON ERROR = 'CONTINUE' is better than SKIP FILE since it is preferable to ingest valid data in file even some has issues. Option C uses external tables combined with streams and tasks or dynamic tables which is an alternative to Snowpipe and COPY INTO and also provides automatic ingestion and transformation capabilities. Option A is less effective because it does not provide a mechanism to capture and log errors from the copy process; skipping files provides no insight to the validity of data. Option D is not the most efficient. While UDFs can handle complex transformations, relying solely on them for all validation and transformation steps can lead to performance bottlenecks and introduce maintenance overhead; Also setting ON_ERROR = 'SKIP_FILE' isn't a great pattern if you want to ingest partial data. Option E's BEFORE trigger might add significant overhead since Snowflake triggers have limitations.

NEW QUESTION # 35
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?

A. They offer real-time updates reflecting instantaneous database changes.
B. Regular views simplify complex data structures for better query performance.
C. Materialized views restrict data retrieval for improved security.
D. Materialized views provide precomputed snapshots, improving query performance.

Answer: D

Explanation:
Materialized views provide precomputed snapshots, enhancing query performance.

NEW QUESTION # 36
When maintaining reports and dashboards, why is it crucial to build automated and repeatable tasks?

A. Automated tasks reduce manual efforts, ensuring consistency.
B. Repeatable tasks hinder data updates in dashboards.
C. Automated tasks increase the complexity of dashboard management.
D. They ensure inconsistency in reports and dashboards.

Answer: A

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.

NEW QUESTION # 37
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