DAA-C01 Unterlage, DAA-C01 Prüfungsunterlagen

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
Topic 2: Build and Troubleshoot Advanced SQL Queries	20–25%	- Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing
Topic 3: Prepare and Present Data	10–15%	- Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements
Topic 4: Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
Topic 5: Perform Simple Data Transformations for Analysis	15–20%	- Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs
Topic 6: Perform Descriptive and Diagnostic Analysis	10–15%	- Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis
Topic 7: Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling

>> DAA-C01 Unterlage <<

DAA-C01 Prüfungsunterlagen & DAA-C01 Online Prüfung

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q20-Q25):

20. Frage
A company receives daily CSV files containing customer order data'. Each file contains a header row and is compressed using GZIP.
The files are landed in an AWS S3 bucket. The company wants to automate the data ingestion into a Snowflake table named 'orders table'. The requirements are: 1. Automated ingestion: New files should be automatically ingested as they arrive in the S3 bucket. 2. Data validation: Records with invalid dates or missing product IDs should be rejected and logged for review. 3. Data transformation: The column (string format 'YYYY-MM-DD') needs to be converted to a DATE data type, and a new column 'order _ year' needs to be derived from the 'order_date'. Which combination of Snowflake features and configurations provides the MOST efficient and reliable solution to meet these requirements?

A. Create a Snowpipe that points to the S3 bucket. Use a COPY INTO statement with 'VALIDATE(O)' and a BEFORE trigger to invoke a stored procedure that validates the data against a set of rules. Use a stored procedure to transform the data into 'orders_table' .
B. Create an external table pointing to the S3 bucket. Use a stream on the external table to track changes and a task to periodically move the new data into the 'orders_table' while performing the necessary transformations and validations. This could also be achieved using Dynamic Tables.
C. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that includes 'ON ERROR = 'SKIP_FILE". Use a downstream task to periodically validate and transform the data in the 'orders_table'.
D. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that utilizes a user-defined function (UDF) written in Python to perform complex data validation and transformation before loading the data into the 'orders_table'. Set 'ON_ERROR = 'SKIP_FILEP to avoid loading erroneous data.
E. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that performs the date conversion using TO DATE() and extracts the order year using YEAR(). Configure the COPY INTO statement with "ON_ERROR = 'CONTINUE" and a validation table to log rejected records.

Antwort: B,E

Begründung:
Options B and C offer the best combination of features to address the requirements effectively. Option B leverages Snowpipe's COPY INTO statement to directly convert the date, calculate order year, and handle errors by continuing the load and logging invalid records into a validation table. This maximizes efficiency and ensures that valid data is ingested quickly. ON ERROR = 'CONTINUE' is better than SKIP FILE since it is preferable to ingest valid data in file even some has issues. Option C uses external tables combined with streams and tasks or dynamic tables which is an alternative to Snowpipe and COPY INTO and also provides automatic ingestion and transformation capabilities. Option A is less effective because it does not provide a mechanism to capture and log errors from the copy process; skipping files provides no insight to the validity of data. Option D is not the most efficient. While UDFs can handle complex transformations, relying solely on them for all validation and transformation steps can lead to performance bottlenecks and introduce maintenance overhead; Also setting ON_ERROR = 'SKIP_FILE' isn't a great pattern if you want to ingest partial data. Option E's BEFORE trigger might add significant overhead since Snowflake triggers have limitations.

21. Frage
A Data Analyst executes a query in a Snowflake worksheet that returns the total number of daily sales, and the total amount for each sale. How can the Analyst check the distribution of the total amount, without running the query again?

A. Call the WIDTH_BUCKET function.
B. Click on the column header in the results and review the histogram.
C. Go to Chart and select a bar chart that contains the two variables.
D. Go to Chart and select a histogram that includes the two variables.

Antwort: B

Begründung:
One of the most powerful features of the Snowsight interface for a Data Analyst is the automatic data profiling provided in the results pane. Snowflake automatically calculates statistics and visual distributions for the result set of any query executed in a worksheet, provided the result set is not excessively large.
When the Analyst views the query results, they can simply click on the column header for the "total amount" column. This action opens a summary pane that displays key descriptive statistics such as the mean, sum, and a histogram showing the frequency distribution of the values in that specific column. This allows for immediate visual analysis of data skew, outliers, or common ranges without requiring the analyst to write additional SQL or move the data to an external visualization tool.
Evaluating the Options:
* Option A is the Correct answer. This is the fastest, built-in way to perform "exploratory data analysis" (EDA) on a result set within the UI.
* Option B and C are incorrect because while Snowsight does have a "Chart" tab, creating a chart requires manual configuration and is a separate step from the automatic profiling features found in the column headers.
* Option D is incorrect because calling the WIDTH_BUCKET function would require the Analyst to run the query again with modified SQL logic, which explicitly contradicts the requirements of the question.
This feature significantly enhances the Data Analysis workflow by providing "at-a-glance" insights into data quality and distribution directly within the development environment.

22. Frage
You are tasked with enriching a 'SALES DATA' table in Snowflake with geographic information based on IP addresses. You have access to an external function 'GEO LOOKUP(ip_address)' that returns a JSON object containing geographical details (city, region, country) for a given IP address. The 'SALES DATA' table contains 'SALE D', 'CUSTOMER D', ADDRESS', and 'SALE AMOUNT columns. You need to enrich the table with city and country information derived from the IP address. Which of the following statements will correctly add 'CITY' and 'COUNTRY columns to a new table 'ENRICHED SALES DATA based on the external function 'GEO LOOKUP , correctly handling potential NULL values and ensuring data type consistency?

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

Antwort: C

Begründung:
Option D is the most robust solution. It extracts the city and country values from the JSON object returned by the function using the operator. The cast ensures the data is stored as strings. Critically, it uses to handle cases where the 'GEO_LOOKUP' function might return NULL (e.g., for invalid IP addresses), preventing errors and providing a default value ('Unknown'). Option A does not handle NULL, Option B's 'GET_PATH' is not a standard Snowflake function for JSON parsing, Options C parses the GEO LOOKUP output to json format if its not which can result in the 'CITY' and 'COUNTRY becoming 'NULL'. The option E 'PARSE_JSON' would throw errors on invalid json strings in the ip address.

23. Frage
When creating reports and dashboards, how does evaluating data based on business requirements impact the visualization process?

A. It limits data selection, affecting overall dashboard quality.
B. Evaluating data ensures relevant and useful dashboard content.
C. Business requirements have no impact on data selection for visualization.
D. Evaluating data complicates dashboard creation.

Antwort: B

Begründung:
Evaluating data based on business requirements ensures the dashboard contains relevant and useful content, improving its quality.

24. Frage
You have a table 'CUSTOMER DATA with sensitive information like 'SSN' and 'CREDIT CARD NUMBER. You need to provide access to analysts for reporting purposes but must mask the sensitive columns. You decide to use a combination of Secure Views and data masking policies. Consider the following code snippets:

An analyst with the 'ANALYST ROLE executes the following query: SELECT FROM ANALYST CUSTOMER VIEW LIMIT 10; What will be the value of the 'SSN' column in the result set for the analyst?

A. An error will be thrown because a secure view cannot display a masked column.
B. An error will be thrown because the analyst does not have direct access to the table.
C. The actual SSN value from the 'CUSTOMER DATA' table.
D. '--'
E. NULL

Antwort: D

Begründung:
The masking policy 'mask_ssn' is applied to the 'SSN' column in the 'CUSTOMER DATA' table. Because the analyst executing the query has the 'ANALYST_ROLE, the masking policy will transform the 'SSN' value to - before the result set is returned. Secure Views can access masked columns and display them according to the policy definition. The secure view ensures that the analyst can only access the data through the view, further enhancing security.

25. Frage
......

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