DAA-C01 Practice Exam Pdf - Questions DAA-C01 Pdf

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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. Snowsight dashboards 2. Present analytical results - Integrate with BI tools 1. Power BI integration 2. Other partner visualization tools 3. Tableau integration
Topic 2: Data Ingestion and Data Preparation	15%-20%	- Implement data processing solutions 1. Automate and implement data pipelines (scheduling) 2. Use logging and monitoring solutions (auditing, data lineage) 3. Respond to processing failures 4. Cleanse, conform, and enrich data - Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Implement constraints 3. Define primary keys for tables - Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Retrieve data from structured sources (CSV) 4. Synthetic Data Generation - 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. Determine the level of data granularity required 2. Evaluate required transformations (table joins, set operations, ASOF JOINS) 3. Identify elements required for business goals using BI reports or SQL analysis 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 data from external/internal stages into a table 2. Load files using Snowsight
Topic 3: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Time-series analysis 3. Aggregate functions
Topic 4: Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Snowflake schema design 3. Star schema design - Transform data using SQL 1. Common Table Expressions (CTEs) 2. PIVOT/UNPIVOT operations 3. Window functions 4. QUALIFY clauses

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
You have a Snowflake table named 'sensor_data' with a column 'reading' containing JSON data'. The JSON structure varies, but you want to extract a specific nested value, 'temperature', using a UDE The path to 'temperature' might be different depending on the 'sensor_type'. Some sensors have the temperature at '$.metrics.temperature' , others at '$.reading.temp_c'. The sensor type is stored in the 'sensor_type' column. You want to create a UDF named which takes the JSON 'reading' and the 'sensor_type' as input and extracts the temperature, returning NULL if the path does not exist in the JSON. How can you implement this using a JavaScript UDF and Snowflake's JSON parsing functions for optimal performance?

A. Option B
B. Option E
C. Option D
D. Option A
E. Option C

Answer: E

Explanation:
Option C is the MOST optimal. It uses 'VARIANT as the input data type for 'reading' , which avoids the unnecessary parsing of JSON using as Snowflake automatically parses JSON data into a VARIANT type. It also includes a 'try...catch' block to handle cases where the specified path does not exist within the JSON, returning 'NULL' as required. This prevents errors from halting the query. Using 'VARIANT directly and exception handling offers superior performance. Option A and D parse VARIANT as String, leading to parsing overhead. B misses the try catch block and is prone to failure when temp is not available for a given sensor. Option E is less efficient than option C due to using array notation.

NEW QUESTION # 18
You have a table named 'sales_data' with a column 'product_details' of type VARIANT containing JSON data for various products. The JSON structure is inconsistent; some products have a 'size' attribute as a string, while others have it as an integer, and some don't have the attribute at all. You need to extract the 'size' as a consistent numeric value (NULL if it's missing) for analysis. Which SQL statement using table functions and data type conversion techniques correctly and efficiently handles this data inconsistency?

Answer: B

Explanation:
TRY_TO_NUMBER() is the most robust and concise way to handle potential data type inconsistencies and missing values when extracting data from VARIANT columns. It attempts to convert the value to a NUMBER and returns NULL if the conversion fails. This elegantly handles both string and integer representations of 'size', as well as cases where 'size' is missing from the JSON. Using Case statements or IS_NUMBER requires more verbose logic and can be less efficient than using the built-in function. NVL replaces NULL with 0, which isn't suitable as the question asks for NULL if missing.

NEW QUESTION # 19
You are a data analyst at a retail company. You need to calculate the total sales for each product category, but only for categories where the average unit price is above $50 and the number of sales transactions exceeds 1000. Which Snowflake SQL query would efficiently achieve this?

Answer: E

Explanation:
Option D correctly uses the HAVING clause to filter aggregated results based on both the average unit price and the count of transactions. Option A does not consider distinct transaction_id, Option B includes the aggregate functions in where clause which is not valid, option C uses subquery which adds overhead, option E uses QUALIFY that is not suitable to this scenario.

NEW QUESTION # 20
You're working with a large dataset containing website user activity, including 'session_id', 'user_id', 'timestamp', and 'page_view'. You suspect bot activity is skewing your engagement metrics. Bots tend to have very short session durations and high page view counts within those sessions. Which of the following Snowflake SQL queries, used in combination, would be MOST effective in identifying and flagging potential bot sessions, considering performance on a large dataset? Assume you have access to Snowflake's statistical functions.

Answer: E

Explanation:
Option E is most effective because it identifies bot sessions based on two distinct criteria: unusually high page view counts AND unusually short session durations, which are characteristic of bots. By combining these two conditions with standard deviations, it reduces the likelihood of false positives. All other answer choices only check rate and not the count or session duration .

NEW QUESTION # 21
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?

A. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
B. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
C. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
D. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
E. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.

Answer: B

Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

NEW QUESTION # 22
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