Will Snowflake DAA-C01 Practice Questions help You to Pass the certification exam?

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
What role does operationalizing data play in maintaining reports and dashboards for business requirements?

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

Answer: C

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

NEW QUESTION # 41
How can automated and repeatable tasks contribute to maintaining reports and dashboards in meeting business requirements?

A. Automated tasks ensure consistency and reduce manual effort.
B. They solely increase the complexity of dashboard management.
C. They limit the scalability of dashboards and reports.
D. Repeatable tasks hinder data updates in dashboards.

Answer: A

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.

NEW QUESTION # 42
You are tasked with building a data ingestion pipeline to retrieve data from a transactional database using Change Data Capture (CDC).The source database is a MySQL instance. Which of the following approaches is MOST suitable for efficiently retrieving and loading the changed data into Snowflake while minimizing latency?

A. Use a third-party CDC tool that supports MySQL as a source and Snowflake as a target. Configure the tool to capture changes from the MySQL binary logs and stream them directly into Snowflake tables.
B. Create a Snowflake external table pointing to the MySQL database. Use the external table to read data directly from the MySQL database into Snowflake tables.
C. Configure MySQL binary log replication to a cloud storage location (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage) and use Snowpipe to continuously ingest the binary log files into Snowflake.
D. Create a Snowpark Python UDF that connects to the MySQL database, reads the binary logs, and applies the changes directly to Snowflake tables.
E. Use a scheduled task to periodically query the MySQL database for records modified within a specific time window and load them into Snowflake using COPY INTO. Implement a timestamp-based or version-based change tracking mechanism in the query.

Answer: A,C

Explanation:
Options B and C are the most efficient and reliable for CDC. Option B leverages the MySQL binary logs directly. Configuring replication to cloud storage and using Snowpipe provides a continuous and near real-time ingestion pipeline. Option C, using a dedicated CDC tool, offers a managed solution that handles the complexities of binary log parsing, change tracking, and data transformation. Option A is less efficient due to the need for periodic polling and potential data loss if changes occur between polls. Option D involves significant coding complexity and potential performance issues. Option E is generally not recommended for transactional data due to performance limitations.

NEW QUESTION # 43
A data analyst is building a dashboard in Tableau connected to Snowflake. The dashboard needs to display the average order value (AOV) for each product category over the last quarter. The Snowflake table 'ORDERS contains columns: 'ORDER ID' , 'ORDER DATE', 'PRODUCT CATEGORY' , and 'ORDER VALUE'. The analyst wants to minimize data transfer from Snowflake to Tableau and optimize dashboard performance. Which of the following approaches is MOST efficient?

A. Use Tableau's Custom SQL feature to execute a query that calculates the AOV directly in Snowflake and retrieves only the aggregated results.
B. Create a Snowflake view that calculates the AOV and connect Tableau to the view.
C. Schedule a daily task to export AOV data from Snowflake into a CSV file, and use Tableau to read from the CSV file.
D. Extract all data from the 'ORDERS' table into Tableau and calculate the AOV within Tableau.
E. Create a Snowflake stored procedure to calculate the AOV and call the stored procedure from Tableau using ODBC connection.

Answer: A,B

Explanation:
Both using Tableau's Custom SQL and creating a Snowflake view are efficient options. Custom SQL allows pushing the calculation to Snowflake, minimizing data transfer and leveraging Snowflake's compute power. Creating a view is also a very good approach since it pre-calculates and store the result set as a view. Extracting all data into Tableau is inefficient due to high data transfer. Using a CSV file adds complexity and latency. Stored procedures are typically not designed for direct dashboard interaction; they are better suited for data transformation pipelines.

NEW QUESTION # 44
When connecting BI tools to Snowflake for dashboard creation, what considerations are crucial for seamless integration? (Select all that apply)

A. Data encryption requirements
B. Network latency between BI tool and Snowflake
C. Availability of a Snowflake account only
D. Compatibility of BI tool with Snowflake

Answer: A,B,D

Explanation:
Seamless integration requires considering compatibility, encryption, and network latency between BI tools and Snowflake.

NEW QUESTION # 45
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