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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Ingestion and Data Preparation15%-20%- Perform data discovery to identify what is needed from available datasets
  • 1. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 2. Determine the level of data granularity required
  • 3. Identify elements required for business goals using BI reports or SQL analysis
  • 4. Query tables to assess data elements and statistics maintained by Snowflake
  • 5. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
- Implement data processing solutions
  • 1. Use logging and monitoring solutions (auditing, data lineage)
  • 2. Automate and implement data pipelines (scheduling)
  • 3. Respond to processing failures
  • 4. Cleanse, conform, and enrich data
- Use a collection system to retrieve data
  • 1. Retrieve data from unstructured sources
  • 2. Retrieve data from structured sources (CSV)
  • 3. Synthetic Data Generation
  • 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
- Use best practice considerations relating to data integrity structures
  • 1. Define primary keys for tables
  • 2. Implement constraints
  • 3. Perform table joins between parent/child tables
- 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
- Prepare data and load into Snowflake
  • 1. Load data from external/internal stages into a table
  • 2. Load files using Snowsight
Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Snowflake-specific analytical features
  • 3. Aggregate functions
Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design
- Transform data using SQL
  • 1. QUALIFY clauses
  • 2. PIVOT/UNPIVOT operations
  • 3. Window functions
  • 4. Common Table Expressions (CTEs)
Data Presentation and Data Visualization28%-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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
When considering row access policies and Dynamic Data Masking in Snowflake, how do they influence data visibility and security?

Answer: B,D

Explanation:
Row access policies and Dynamic Data Masking play vital roles in limiting data access based on user roles and dynamically masking sensitive data to enhance data security and visibility for authorized users.


NEW QUESTION # 50
You are tasked with building a near real-time data pipeline that ingests streaming data from Kafka into Snowflake. The data is semi- structured JSON. The goal is to transform and load only records with a 'status' field equal to 'active' into a table named 'ACTIVE RECORDS'. Which approach provides the most efficient and scalable solution, minimizing latency and Snowflake resource consumption?

Answer: B

Explanation:
Snowpipe with a transformation in the COPY INTO statement offers the most efficient and scalable near real-time data ingestion solution. This approach allows filtering during the load process, avoiding the need to load all data and then filter later. Option A involves a scheduled task, which introduces latency. Option C's external table + stream approach is generally slower for continuous ingest. Option D adds the overhead and cost of a 3rd party ETL tool, while Option E's materialized view would work, it isn't the most efficient approach for the initial data filtering.


NEW QUESTION # 51
Which actions are pertinent in identifying demographics and relationships during diagnostic analysis? (Select all that apply)

Answer: A,D

Explanation:
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships during diagnostic analysis.


NEW QUESTION # 52
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

Answer: C

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.


NEW QUESTION # 53
A Snowflake data analyst needs to create a secure view called "masked customer data' based on an existing table named 'customer data'. The requirement is to mask the 'email' column for all users except those with the "DATA ADMIN' role. Also, only users with the 'ANALYST' role should be able to query any data from the view. The masking policy 'email_mask' has already been created. Which of the following sequence of commands correctly implements this requirement?

Answer: D

Explanation:
Option C correctly implements the requirements. First, a 'SECURE VIEW' is created to ensure data security. Then, the 'ALTER VIEW' command correctly applies the masking policy to the column of the view. Finally, 'GRANT SELECT ensures that only the 'ANALYST role can query data from the view. Option B attempts to alter the underlying table directly which isn't the intention, and uses an invalid command 'MODIFY COLUMN'. Option A does not create a secure view. Option D uses incorrect syntax 'MODIFY COLUMN' for altering view. Option E try to alter the masked_customer_data, which is not a TABLE instead it is a View.


NEW QUESTION # 54
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