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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Present analytical results
  • 2. Snowsight dashboards
- Integrate with BI tools
  • 1. Tableau integration
  • 2. Other partner visualization tools
  • 3. Power BI integration
Topic 2: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Time-series analysis
  • 3. Aggregate functions
Topic 3: Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Snowflake schema design
  • 2. Star schema design
  • 3. Data vault models
- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Window functions
  • 3. QUALIFY clauses
  • 4. Common Table Expressions (CTEs)
Topic 4: Data Ingestion and Data Preparation15%-20%- 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
- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
- 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)
- Implement data processing solutions
  • 1. Cleanse, conform, and enrich data
  • 2. Automate and implement data pipelines (scheduling)
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Respond to processing failures
- 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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 2. Query tables to assess data elements and statistics maintained by Snowflake
  • 3. Determine the level of data granularity required
  • 4. Identify elements required for business goals using BI reports or SQL analysis
  • 5. Evaluate required transformations (table joins, set operations, ASOF JOINS)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
You're using Snowsight to build a dashboard for monitoring website performance. The data is in a table called 'WEB EVENTS' with columns: 'EVENT _ TIME' (TIMESTAMP_NTZ), 'EVENT _ TYPE' (VARCHAR, e.g., 'page_view', 'button_click'), 'USER_ID' (VARCHAR), and 'PAGE URL' (VARCHAR). You want to create a tile that shows the average time between consecutive 'page_view' events for each user over the last 7 days. This will help you understand how users are navigating the site. Assume that for a single user, page_view events are ordered by EVENT TIME. Which of the following SQL queries, when used as the basis for a Snowsight tile, will correctly calculate this average time difference in seconds?

Answer: D

Explanation:
It uses the window function to get the previous event time for each user, then calculates the difference between consecutive event times in seconds using 'TIMESTAMP_DIFF. The outer query then averages these differences for each user. The 'WHERE PREVIOUS_EVENT_TIME IS NOT NULL' clause is important to exclude the first event for each user, which would have a null previous event time. Option B attempts to subtract timestamps directly, which is not the correct way to get the difference in seconds in Snowflake. Option C uses 'DATEDIFF which has the parameters in the wrong order compared to the logic of the question. Option D incorrectly uses FIRST _ VALUE. Option E omits the subquery necessary to correctly use the LAG function.


NEW QUESTION # 21
You are building a data pipeline to ingest customer data into Snowflake. You have identified a need to dynamically determine the data load timestamp during the ingestion process itself, without relying on external systems or pre-defined variables. Which system function(s) would be the MOST appropriate and efficient choice to accomplish this?

Answer: B

Explanation:
The function returns the current timestamp at the start of the statement. 'SYSDATE()' and 'GETDATE()' functions does not exists in Snowflake. is a synonym for CURRENT TIMESTAMP(). However, is not a standard documented function.


NEW QUESTION # 22
You have a Snowflake table named 'LOG DATA' with a "VARIANT' column called 'log_message" storing application logs in JSON format. The 'log_message' structure contains a nested JSON object called 'context' , which may or may not be present in every log entry. The 'context' object itself contains several key-value pairs, including potentially a 'user_id', a 'session_id' , and/or a 'request_id' You need to extract all the unique combination of 'user _ id' , and 'request_id' that exist across the dataset, accounting for instances where the 'context' object or any of the individual IDs are missing (represented as NULL). The goal is to ensure no errors are thrown due to missing fields. Select all the options which would achieve this outcome.

Answer: A,C

Explanation:
Options B and E are both valid solutions. Option B Uses 'TRY TO_STRING' function. If value is not present and is NULL, then automatically it will be converted to Null without any failure. Option E Uses 'COALESCE will handle any missing 'context object or missing IDs within the 'context' object without throwing an error. If any of the three values are NULL then it would be converted to string 'NULL'. Why other options are not entirely correct Option A This is not a correct answer, as a result in case NULL values encountered, this will result in failures. Option C & D This is a valid solution, however, since NVL and IFF functions are slower compared to TRY _ TO STRING and COALESCE, this result in slow processing. So, its not entirely correct answer.


NEW QUESTION # 23
You are investigating why a Snowflake data replication process between two regions is experiencing significant lag. You need to collect data to determine if the issue stems from network latency, insufficient warehouse resources in the target region, or data transformation bottlenecks. Select the data collection methods that will provide the MOST relevant insights.

Answer: A,B,C

Explanation:
Options A, B, and C provide specific data points relevant to the identified potential causes. Monitoring replication lag metrics (A) directly quantifies the lag. Traceroute (B) measures network latency. Analyzing query history (C) identifies transformation bottlenecks. Restarting the process (D) might temporarily resolve the issue but doesn't address the root cause. Snowflake manages the underlying infrastructure; therefore, monitoring VM CPU utilization (E) is not something that a data analyst has access to or is needed for the diagnostic in this case. The Snowflake service runs and manages the queries.


NEW QUESTION # 24
How does performing data discovery through querying tables in Snowflake aid in data preparation?

Answer: C

Explanation:
Querying tables in Snowflake aids in understanding necessary data transformations for effective preparation.


NEW QUESTION # 25
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