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[General] Free PDF Snowflake - DEA-C02 Pass-Sure Sample Questions Pdf

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【General】 Free PDF Snowflake - DEA-C02 Pass-Sure Sample Questions Pdf

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q74-Q79):NEW QUESTION # 74
A financial services company, 'Acme Finance', wants to share aggregated, anonymized transaction data with a research firm, 'Data Insights', through a Snowflake Data Clean Room. Acme Finance needs to ensure that Data Insights can only analyze the data using pre- defined aggregate functions and cannot access the raw, underlying transactional details. Acme Finance has already created a secure view to share the aggregated data'. Which of the following steps are necessary to grant Data Insights access to the data securely while enforcing the required restrictions?
  • A. Create a row access policy that restricts the rows returned based on the role used by Data Insights. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
  • B. Create a share object and grant USAGE privilege on the database containing the secure view to the share. Then, grant SELECT privilege on the secure view to the share. Finally, share the share with Data Insights' Snowflake account using their account identifier.
  • C. Create a masking policy that only allows aggregate functions to be executed by Data Insights' role and apply it to the relevant columns in the underlying table. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
  • D. Create an external function that Data Insights can call to execute pre-approved aggregate functions on the underlying data. Grant USAGE on the function to Data Insights' role and create a secure view that uses that function.
  • E. Grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
Answer: B
Explanation:
Option B correctly outlines the process of creating a share, granting USAGE on the database and SELECT on the secure view to the share, and then sharing the share with the consumer account. This is the fundamental mechanism for secure data sharing in Snowflake. Option A is incorrect because it doesn't utilize sharing. Options C and D are data governance mechanisms (masking and row access policies) that control data visibility within an account, not across accounts. Option E suggests an external function, which is not a standard approach for Data Clean Rooms and adds unnecessary complexity.

NEW QUESTION # 75
You are loading data from an S3 bucket into a Snowflake table using the COPY INTO command. The source data contains dates in various formats (e.g., 'YYYY-MM-DD', 'MM/DD/YYYY', 'DD-Mon-YYYY'). You want to ensure that all dates are loaded correctly and consistently into a DATE column in Snowflake. Which of the following COPY INTO options and commands is the MOST appropriate to handle this?
  • A. Use the 'VALIDATE(O)' command before the COPY INTO command to identify files with invalid date formats and then process them separately.
  • B. Use the 'STRTOK TO DATE function within a SELECT statement in a Snowpipe transformation to dynamically parse the dates based on different patterns.
  • C. Use the 'ON_ERROR = 'SKIP FILE" option to skip files with invalid date formats.
  • D. Utilize the 'DATE' function with explicit format strings inside a Snowpipe transformation pipeline. This involves pattern matching using 'CASE WHEN' statements to identify date formats before converting to the DATE data type.
  • E. Use the 'DATE FORMAT option in the COPY INTO command with a single format string that covers all possible date formats.
Answer: D
Explanation:
Option E is the most robust. It uses regular expressions for pattern matching to accurately determine the format of each date and then uses TO DATE with the correct format string. This ensures consistent conversion to Snowflake's DATE data type. While Option D aims to use STRTOK TO DATE, Snowflake doesn't offer dynamic date parsing during data loading, so each date pattern will be handled manually using CASE statement approach mentioned in the option E, making it more suitable for a varied array of patterns. Options A, B, and C are inadequate. A single 'DATE FORMAT won't handle multiple formats. B skips files, leading to data loss. C requires manual intervention and doesn't automate the loading process.

NEW QUESTION # 76
Consider the following scenario: You are ingesting JSON data from an external stage into Snowflake. The JSON data contains an array of objects, where each object represents a product with attributes like 'product id', 'name', and 'price'. However, sometimes the 'price' field is missing entirely from some product objects. You want to load this data into a Snowflake table with columns 'product_id', 'name', and 'price' (defined as NUMBER). How can you handle the missing 'price' field gracefully during the COPY INTO operation, ensuring that missing prices are represented as NULL in the Snowflake table without causing errors?
  • A. Define the 'price' column in the Snowflake table as VARIANT. After the data is loaded, create a view that extracts the price using the 'GET' function and converts it to NUMBER using 'TO NUMBER. Handle NULL values in the view using 'price')), NULL, TO 'price')))'.
  • B. Pre-process the JSON data before loading into Snowflake, adding a 'price': null field to any product object missing the price.
  • C. Define the 'price' column in the Snowflake table as NUMBER and use the DEFAULT NULL clause. The COPY INTO statement will automatically insert NULL values for missing fields.
  • D. Define the 'price' column in the Snowflake table as NUMBER and use a transformation within the COPY INTO statement to handle missing prices: 'TRANSFORMATION = (price =
  • E. Define the 'price' column in the Snowflake table as VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=
Answer: D
Explanation:
Option B is the most effective approach using Snowflake's built-in features. TRY TO NUMBER will return NULL if 'price' is missing, ensuring that NULL is inserted into the NUMBER column, avoiding errors. Option A does not work, DEFAULT NULL is not respected on column mappings. C does not work since VARCHAR and NULLIFEMPTY do not meet all the requirments. Option D works, but is not the most efficient. Option E requires unnecessary pre-processing.

NEW QUESTION # 77
You are developing a JavaScript stored procedure in Snowflake using Snowpark to transform data'. The procedure needs to efficiently calculate the sum of 'amount' for each 'customer_id' in a large table named 'orders'. You want to avoid transferring large amounts of data to the client and utilize Snowpark's pushdown capabilities. Which of the following JavaScript code snippets is the MOST efficient and correct way to achieve this? Assume 'snowflake' binding is available.
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: E
Explanation:
Option B is the most efficient because it leverages Snowpark's 'groupBy' and 'sum' functions, pushing the aggregation computation to the Snowflake engine. This avoids transferring all the raw data to the client-side JavaScript environment, minimizing network traffic and improving performance. Options A, C and D are not leveraging snowpark dataframe. E uses deprecated methods

NEW QUESTION # 78
A data engineering team is managing a Snowflake warehouse that supports a high volume of ad-hoc queries from data analysts exploring a large, semi-structured JSON dataset containing website clickstream data'. The query  performance is frequently slow, and analysts are complaining about long wait times. The warehouse is already sized appropriately. You have identified that many of the queries filter on nested JSON attributes that are not explicitly indexed. Considering only query acceleration service features, what is the MOST effective approach to improve query performance for these ad-hoc queries without modifying the queries themselves or significantly increasing storage costs?
  • A. Create a dedicated virtual warehouse specifically for ad-hoc queries, and enable query acceleration on this warehouse.
  • B. Enable Automatic Clustering on the table to improve data organization based on query patterns.
  • C. Use a combination of materialized views and query acceleration for best performance.
  • D. Enable the Materialized View feature to create materialized views over the frequently queried nested JSON attributes.
  • E. Enable Search Optimization Service for the table containing the JSON data. Selectively enable search optimization on the specific columns and nested paths that are frequently used in WHERE clause predicates.
Answer: E
Explanation:
Search Optimization Service is designed specifically to improve query performance on semi-structured data and complex predicates, especially on JSON data. It automatically creates and maintains search access paths, including paths for nested JSON attributes, enabling faster filtering and retrieval of relevant data. Materialized Views are beneficial, but require creation and maintenance, and might not be ideal for ad-hoc queries. Automatic Clustering helps with data organization, but its impact on complex JSON queries might be limited. Using Query Acceleration alone requires a larger warehouse and may not address the underlying issue of unoptimized queries on semi-structured data. While a dedicated warehouse is a good practice, it does not address the underlying performance issue related to JSON queries.

NEW QUESTION # 79
......
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