Free PDF Quiz Snowflake - DAA-C01 - SnowPro Advanced: Data Analyst Certification Exam Accurate Certification Sample Questions

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Present analytical results 2. Snowsight dashboards - Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools
Topic 2: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. Window functions 2. QUALIFY clauses 3. PIVOT/UNPIVOT operations 4. Common Table Expressions (CTEs) - Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design
Topic 3: Data Ingestion and Data Preparation	15%-20%	- Implement data processing solutions 1. Respond to processing failures 2. Automate and implement data pipelines (scheduling) 3. Cleanse, conform, and enrich data 4. Use logging and monitoring solutions (auditing, data lineage) - Perform data discovery to identify what is needed from available datasets 1. Evaluate required transformations (table joins, set operations, ASOF JOINS) 2. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 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 - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use a collection system to retrieve data 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 2. Retrieve data from structured sources (CSV) 3. Synthetic Data Generation 4. Retrieve data from unstructured sources - 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. 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
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Snowflake-specific analytical features 3. Time-series analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
When dealing with JSON data in Snowflake and utilizing built-in functions for traversing, flattening, and nesting, what complexities might arise? (Select all that apply)

A. Complexity in handling nested JSON structures
B. Difficulties in parsing and querying nested JSON objects
C. Limitations in processing specific JSON data sizes
D. Challenges in handling specific data types only

Answer: A,B

Explanation:
Handling nested JSON structures and complexities in parsing and querying nested JSON objects might pose challenges when manipulating JSON data in Snowflake using built-in functions.

NEW QUESTION # 44
When employing window functions versus table functions in Snowflake, how do they differ in their application and output?

A. Window functions operate on entire datasets and generate tables as output
B. Window functions provide aggregate results and modify table structures directly
C. Table functions return tables as their output
D. Table functions process data within specified partitions or frames

Answer: D

Explanation:
Table functions in Snowflake process data within specified partitions or frames, whereas window functions generate aggregate results and operate on entire datasets, differing in their scope and operation.

NEW QUESTION # 45
When dealing with semi-structured data in Snowflake, what advantages do native data types offer over traditional relational data types?

A. Native data types enforce strict schema requirements
B. Native data types offer higher data integrity and consistency
C. Native data types limit query complexity and optimization
D. Native data types provide flexibility in handling varied data structures

Answer: D

Explanation:
Native data types in Snowflake offer flexibility in handling varied structures of semi-structured data, allowing for a more adaptable approach compared to traditional relational data types.

NEW QUESTION # 46
What distinguishes exploratory ad-hoc analyses from routine data analysis?

A. They query known patterns without further exploration.
B. Ad-hoc analyses focus only on established trends.
C. Ad-hoc analyses rely solely on predefined queries.
D. Ad-hoc analyses explore patterns and anomalies beyond established routines.

Answer: D

Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.

NEW QUESTION # 47
You are designing a dimensional model for a subscription-based service. You have a 'FACT SUBSCRIPTIONS' table with columns like 'subscription_id', 'customer id', 'start date', 'end date', and 'subscription_amount'. The business wants to analyze monthly recurring revenue (MRR) and churn rate. You need to model the temporal aspect of subscriptions to accurately calculate these metrics. Select the TWO best approaches to model the time dimension to facilitate these calculations:

A. Create a snapshot fact table FACT SUBSCRIPTION SNAPSHOTS that captures the state of each subscription at the end of each month. This table would include 'subscription_id' , 'customer_id', 'snapshot_date', 'is_active' , and 'subscription_amount' .
B. Create a 'DIM MONTH' table with columns like 'month id', 'month start date', and 'month end date' and link the 'FACT SUBSCRIPTIONS table to it based on the 'start_date' falling within the month.
C. Store 'start_date' and 'end_date' as VARCHAR columns in the 'FACT SUBSCRIPTIONS table to avoid data type conversions.
D. Create a 'DIM_SUBSCRIPTIOW table with 'subscription_id' as the primary key and store all subscription details there, avoiding the need for a fact table.
E. Create a 'DIM_DATE table and link 'FACT_SUBSCRIPTIONS' to it using 'start_date' and 'end_date' columns.

Answer: A,B

Explanation:
Creating a snapshot fact table (option B) allows for direct calculation of MRR and churn at a specific point in time. Analyzing subscription state at monthly intervals is helpful for these metrics. Creating a 'DIM_MONTH' table (option E) simplifies grouping and aggregation of subscriptions by month. Option A (linking to 'DIM DATE' using both start and end dates) might be useful for other types of analysis, but not directly for MRR/churn calculations. Storing dates as VARCHAR (option C) is bad practice and will hinder performance. A dimension table for subscriptions (option D) won't capture the temporal changes necessary for these metrics.

NEW QUESTION # 48
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