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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Loading and Unloading	- Data export 1. UNLOAD and external stages - Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts
Topic 2: Security, Governance, and Data Sharing	- Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC) - Data sharing and governance 1. Secure data sharing 2. Data masking and policies
Topic 3: Data Transformation and Analysis	- SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions - Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching
Topic 4: Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Topic 5: Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas

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

NEW QUESTION # 15
You have a table 'product_catalog' containing a 'description' column of type TEXT, and a 'tags' column which is a VARIANT containing an array of strings representing tags associated with the product. You need to build an efficient search mechanism that allows users to find products matching specific tags. Considering scalability and performance for large catalogs, which of the following methods using table functions and Snowflake's search capabilities would be most suitable? Choose all that apply.

A. Use a Java UDF to iterate over the 'tags' array and check if any of the tags match the search terms. Apply this UDF in a WHERE clause along with a CONTAINS() check on the 'description'
B. Create a search optimization service on the 'product_catalog' table including the 'description' and 'tags' columns. When querying, use a combination of CONTAINS() for 'description' and ARRAY_CONTAINS() on the 'tags' column and a 'SEARCH' clause to filter results.
C. Create a search optimization service on the 'product_catalog' table including the 'description' and 'tags' columns. Use LATERAL FLATTEN to expand the 'tags' array and then create an index on the flattened 'tag' values.
D. Create a view that flattens the 'tags' array using LATERAL FLATTEN into a 'tag' column, and then create a full-text index on the 'description' column. Query the view using CONTAINS() or LIKE operator on the 'description' and EQUALS operator on the 'tag' column.
E. Create a search optimization service on the 'product_catalog' table including the 'description' column. When querying, use a combination of CONTAINS() for 'description' and ARRAY_CONTAINS() on the 'tags' column.

Answer: B,E

Explanation:
Search optimization service in Snowflake is designed to accelerate search queries and is best practice here. Using 'ARRAY on the 'tags' column lets you directly check if the array contains specific tags. Using on the 'description' column can search for specific search terms in your description. Using a 'SEARCH' clause can improve search performnce significantly. Option C and E, are both correct, since they use contains as well as the array_contains but option E includes the use of Search which is more effiecient. Option A is incorrect, as indexes are not allowed on flattened data. UDF will have performance issues. Creating a view and indexing the view is not optimal as querying directly with CONTAINS on the tags column gives faster results.

NEW QUESTION # 16
When sharing views across accounts, which function should be used on the row access policy for the secure views to give users access to rows in a base table?

A. CURRENT_ROLE
B. CURRENT_ACCOUNT
C. CURRENT_AVAILABLE_ROLES
D. CURRENT_USER

Answer: B

Explanation:
In the context of Snowflake Data Sharing, the security model changes because the users and roles accessing the data do not exist in the provider's account. When a provider shares a Secure View that is protected by a Row Access Policy (RAP), standard functions like CURRENT_ROLE or CURRENT_USER are often insufficient or return different values than expected because they reflect the consumer's local environment.
To effectively filter data for different consumers within a single shared table, the CURRENT_ACCOUNT() function is the primary tool. This function returns the unique account identifier of the system currently accessing the data. By including CURRENT_ACCOUNT() in the row access policy logic (e.g., WHERE shared_account_id = CURRENT_ACCOUNT()), the data provider can ensure that each consumer account only sees the specific subset of rows intended for them.
Evaluating the Options:
* Option A and D: CURRENT_ROLE and CURRENT_AVAILABLE_ROLES are generally used for internal account security. When data is shared, the roles in the consumer account do not map to the provider's roles, making these functions unreliable for cross-account row-level filtering.
* Option C: CURRENT_USER is likewise restricted to the local account's user metadata.
* Option B: Is the 100% correct answer. It is the specific system function designed to identify the consumer account in a data-sharing relationship, allowing for robust multi-tenant data protection. This is a vital skill for the Data Governance and Security domain, ensuring that "Secure Data Sharing" remains truly secure.

NEW QUESTION # 17
How do Snowsight dashboards facilitate the presentation of data for business use analyses?

A. Snowsight doesn't support visual data representation.
B. Snowsight dashboards are exclusively text-based, limiting analyses.
C. They enable diverse data representation for effective analyses.
D. Snowsight limits data representation options, hindering analyses.

Answer: C

Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.

NEW QUESTION # 18
In Snowflake, how does leveraging Parquet format contribute to optimizing query performance and storage efficiency?

A. Enhances query performance and reduces storage requirements
B. Parquet format accelerates only metadata retrieval
C. Limits query execution time
D. Impedes data compression and decompression capabilities

Answer: A

Explanation:
Parquet format optimizes query performance and storage efficiency by enhancing data compression, reducing storage needs, and accelerating query execution in Snowflake.

NEW QUESTION # 19
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

A. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.
B. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
C. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
D. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.
E. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.

Answer: B,C

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.

NEW QUESTION # 20
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