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正確的なDAA-C01試験勉強書｜有効的なSnowPro Advanced: Data Analyst Certification Exam

DAA-C01試験の質問に協力して、DAA-C01試験に合格し、DAA-C01証明書を正常に取得することをお約束します。以前のお客様に対する最近の調査によると、99％のSnowflakeお客様が目標を達成できるため、最終的な目標の達成を支援するお手伝いができると考えています。ベッドサイドには、新しい知識の開発を管理するための高品質のDAA-C01テストガイドがあるため、すべてのSnowPro Advanced: Data Analyst Certification Exam学習ポイントをバランスよく把握できます。

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一般的な教育トレーニングソフトウェアとは異なり、DAA-C01試験の質問では、学生がシミュレーション問題を提供するプラットフォームで20〜30時間練習するだけでよいため、DAA-C01試験に合格する自信があります。一部の労働者にとって、それはどれほど効率的か。時は金なりです。今日では効率にますます注意を払っています。適切な場所で時間を使い、低い時間で見返りに高いスコアを得る必要があります。DAA-C01最新の試験トレントはこれを行うのに非常に良いです。

Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q96-Q101):

質問 # 96
A financial institution has implemented both Row Access Policies and Dynamic Data Masking. The Row Access Policy restricts access to transaction data based on the user's department (e.g., 'Fraud Detection', 'Compliance'). Dynamic Data Masking is applied to the 'ACCOUNT NUMBER column, masking all but the last four digits. The institution wants to build a report that shows the distribution of transaction amounts across different departments. Analysts in the 'Compliance' department need to be able to see the full, unmasked 'ACCOUNT NUMBER when investigating potential regulatory violations for transactions within their department only , while still adhering to the Row Access Policy. Which of the following approaches is the MOST secure and compliant way to implement this?

A. Grant the 'Compliance' role the global 'UNMASK' privilege. This will bypass the dynamic data masking policy on the 'ACCOUNT_NUMBER column whenever a Compliance user queries the 'TRANSACTIONS' table, while still respecting the Row Access Policy.
B. Create a stored procedure that executes with 'CALLER rights. Within the stored procedure, use a Snowflake Scripting block to check the user's role and department affiliation. If the user is in 'Compliance' and the transaction is within their department, retrieve the unmasked 'ACCOUNT_NUMBER directly from the underlying table; otherwise, retrieve the masked value. Grant the 'Compliance' role execute privilege on the stored procedure.
C. Modify the Dynamic Data Masking policy on the ' ACCOUNT_NUMBER' column to include a CASE statement that checks if the current user's role is 'Compliance' AND if the Row Access Policy allows access to the relevant department. If both conditions are true, the policy returns the unmasked 'ACCOUNT_NUMBER; otherwise, it applies the masking.
D. Create a custom role hierarchy. Create a role specifically for the 'Compliance' department that inherits from the existing role used by the Row Access Policy. Create a data masking policy based on this new role that unmasks the 'ACCOUNT NUMBER. Grant access to the report to this new role.
E. Create a view that joins the TRANSACTIONS' table with a 'DEPARTMENT_ACCESS' table, which contains mappings between departments and account numbers. Grant the 'Compliance' role 'SELECT' privilege on this view. The view will inherit the masking policy and row access policies, restricting data based on department and masking the account number for all departments except Compliance.

正解：C

解説：
Option B is the most secure and compliant solution. By modifying the Dynamic Data Masking policy to include a CASE statement, you can conditionally unmask the 'ACCOUNT_NUMBER only when both the user is in the 'Compliance' department AND the Row Access Policy grants them access to the relevant transaction data. This ensures that the masking policy is only bypassed when absolutely necessary and that the Row Access Policy remains in effect. Option E is problematic because the UNMASK privilege is too broad and bypasses masking on all tables and columns. Using caller rights (option C) bypasses Row Access Policies too. Role hierarchies don't inherently affect masking. They change object privileges. Option A allows inheritance of the roles that the user is executing, however in the current context, the user will still get blocked due to ROW level security policy.

質問 # 97
What critical role do user-defined functions (UDFs) play in data analysis using SQL?

A. UDFs are limited to specific data types.
B. UDFs hinder query optimization in SQL.
C. UDFs restrict data analysis to predefined operations.
D. They allow customization and extension of SQL functionalities for specific data operations.

正解：D

解説：
UDFs allow customization and extension of SQL functionalities beyond standard operations.

質問 # 98
Your organization receives daily updates to a product catalog in Avro format. The Avro schema evolves frequently, adding and removing fields. You need to ingest these Avro files into Snowflake. Which of the following strategies will BEST handle schema evolution while minimizing data loading complexity?

A. Define a fixed Snowflake table schema with a limited set of commonly used fields. Ignore any additional fields in the Avro files during loading.
B. Use Snowflake's Schema Detection feature with the COPY command into a table with a VARIANT column. Then, use the 'LATERAL FLATTEN' function to extract the fields and load them into a relational table with the correct data types. Automate the schema creation/alteration of the relational table based on the inferred schema from the VARIANT column.
C. Load the Avro files into a CSV format after flattening the Avro Schema.
D. Create a new Snowflake table for each Avro schema version. Maintain a view that unions all tables to provide a consistent interface.
E. Load the Avro files into a VARIANT column in Snowflake and use SQL to extract the required fields. Periodically update the table schema as the Avro schema evolves.

正解：B

解説：
Option D is the best approach. Loading the Avro data into a VARIANT column first allows you to capture all the data, regardless of schema evolution. Using the 'LATERAL FLATTEN' function allows extraction of fields. Utilizing Snowflake's schema detection further automates the process. Option A requires manual schema updates, which is less scalable. Option B creates a management overhead with multiple tables and views. Option C loses data. Option E requires external flattening which creates additional complexity.

質問 # 99
In data presentations for business use analyses, what significance do identifying patterns and trends hold?

A. Patterns and trends don't impact business use analyses significantly.
B. Identifying patterns and trends aids in insightful analyses.
C. It complicates data analysis, hindering decision-making.
D. Recognizing patterns and trends limits data exploration.

正解：B

解説：
Identifying patterns and trends aids in insightful analyses in business use scenarios.

質問 # 100
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

A. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
B. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
C. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
D. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.
E. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.

正解：A、E

解説：
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

質問 # 101
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SnowflakeのDAA-C01試験に受かることは確かにあなたのキャリアに明るい未来を与えられます。SnowflakeのDAA-C01試験に受かったら、あなたの技能を検証できるだけでなく、あなたが専門的な豊富の知識を持っていることも証明します。CertShikenのSnowflakeのDAA-C01試験トレーニング資料は実践の検証に合格したソフトで、手に入れたらあなたに最も向いているものを持つようになります。 CertShikenのSnowflakeのDAA-C01試験トレーニング資料を購入する前に、無料な試用版を利用することができます。そうしたら資料の高品質を知ることができ、一番良いものを選んだということも分かります。

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