DAA-C01 Prüfungsfragen, DAA-C01 Fragen und Antworten, SnowPro Advanced: Data Analyst Certification Exam

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q56-Q61):

56. Frage
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?

A. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
B. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;
C. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
D. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
E. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;

Antwort: B

Begründung:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.

57. Frage
What distinguishes exploratory ad-hoc analyses from routine analysis?

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

Antwort: A

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

58. Frage
You're building a dashboard to monitor the performance of various marketing campaigns. The data resides in Snowflake, and you're using a BI tool that supports direct query The table has columns: 'CAMPAIGN ID, 'DATE, 'IMPRESSIONS', 'CLICKS, 'SPEND , and You need to create a calculated field in the BI tool representing the Cost Per Conversion (CPC), but you want to optimize query performance and avoid division by zero errors. Assume 'SPEND' and 'CONVERSIONS' are both numeric columns. Which SQL expression, suitable for use in a direct query BI tool, is the MOST performant and robust way to calculate CPC, avoiding zero conversion issues?

Antwort: E

Begründung:
The ' DIV0' function is specifically designed by Snowflake to handle division by zero gracefully, returning NULL. It's the most concise and performant way to achieve the desired result. 'NULLIF(CONVERSIONS, 0)' is also correct way, but DIVO is more accurate for Snowflake environment. 'CASE WHEN' and 'IFF are functionally equivalent in this scenario, but is shorter to write. ZEROIFNULL' will return 0 when input is null which won't solve the zero conversion issues. Furthermore ZEROIFNULL' is not a valid Snowflake function.

59. Frage
How does automating and implementing data processing contribute to the overall efficiency of data ingestion?

A. It slows down the data ingestion process significantly.
B. Automating processing hampers data accuracy.
C. Implementing data processing increases data redundancy.
D. Automation eliminates human intervention, ensuring consistency.

Antwort: D

Begründung:
Automation ensures consistency and eliminates manual interventions, enhancing the efficiency of data ingestion.

60. Frage
You are tasked with ingesting data from a REST API that provides customer order information in JSON format. The API returns a nested JSON structure with an array of orders, each containing customer details and order items. The data volume is expected to be high. You need to efficiently load this data into Snowflake. Which of the following approaches would be MOST efficient, considering cost and performance, and taking into account potential data quality issues?

A. Load the data into a temporary stage, manually parse it with SQL statements, and then load it into your target tables. Schedule the task for periodic execution.
B. Use a third-party ETL tool to extract, transform, and load the data into Snowflake.
C. Load the JSON data directly into a VARIANT column, then create a view to flatten the data as needed. Use Snowflake's JSON functions for querying and transformation.
D. Use an external function to call a Python script that parses the JSON, transforms the data into a flat relational structure, and then uses the Snowflake Python Connector to insert the data directly into relational tables.
E. Use Snowpipe with a stream and task to continuously ingest the JSON data into a staging table with a VARIANT column. Then, create a separate task to flatten the data into relational tables using SQL transformations after ingestion.

Antwort: E

Begründung:
Snowpipe with a stream and task offers continuous, near real-time ingestion. Using a staging table with a VARIANT column allows for handling the complex JSON structure initially. Subsequent tasks can then transform and flatten the data into relational tables. This approach combines efficiency, automation, and scalability, minimizing manual intervention and maximizing performance for high-volume data. Other options are less efficient or scalable: A requires constant parsing during queries, C introduces external dependencies and overhead, D adds complexity and cost of a third-party tool, and E is manual and not scalable.

61. Frage
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