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【Hardware】 Knowledge DEA-C02 Points | DEA-C02 Latest Version

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q74-Q79):NEW QUESTION # 74
You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

  • A. Option A
  • B. Option E
  • C. Option B
  • D. Option C
  • E. Option D
Answer: C
Explanation:
Option B correctly addresses all the requirements. It uses 'product_id').cast('string')' to extract the product_id' from the JSON and cast it to a string; filters for non-null 'product_id' values and 'event_time' within the last hour using 'dateadd' and 'current_timestamp' for efficient time filtering; and then groups by 'product_id' to count the events. Option A includes 'to_timestamp' which is unneccessary. Option C extracts the 'product_id' from the JSON string to create 'product_id' without casting it to string type. This could create issues in later aggregations/filtering if the data types do not match up. Option D uses subtraction on 'current_timestamp' with the addition result of 'dateadd('hour', 1, current_timestamp()Y to perform time filtering. This will subtract a future time from the current time. This is incorrect.Option E passes a SQL string directly to the filter, bypassing Snowpark+s function calls.

NEW QUESTION # 75
You are tasked with creating a JavaScript UDF in Snowflake to parse JSON data containing nested arrays of objects. The UDF needs to extract specific values from these nested objects and return them as a comma-separated string. Given the JSON structure below, and the requirement to extract the 'value' field from each object within the 'items' array located inside each element of the 'data' array, which of the following JavaScript UDF definitions will correctly achieve this, assuming the input JSON is passed as a string?

  • A. Option A
  • B. Option B
  • C. Option C
  • D. Option D
  • E. Option E
Answer: E
Explanation:
Option E correctly parses the JSON, iterates through the nested arrays ('data' and 'items'), safely checks for the existence of 'items' and 'value' properties, and extracts the 'value' from each object, joining them into a comma-separated string. This option also gracefully handles the case where 'items' or 'value' might be missing. Options A, B and C are close, but can raise error if data element is missing. Option D returns an array.

NEW QUESTION # 76
Which of the following statements are accurate regarding the differences between SQL UDFs and Java UDFs in Snowflake? (Select two)
  • A. SQL UDFs and Java UDFs are interchangeable, and there is no performance difference between them.
  • B. SQL UDFs can only be used for simple transformations and cannot execute external calls, while Java UDFs can perform complex logic and interact with external services via libraries.
  • C. Java UDFs always execute faster than SQL UDFs due to JVM optimizations.
  • D. Java UDFs are deprecated and should not be used; instead, SQL UDFs are recommended for all scenarios.
  • E. SQL UDFs are defined using SQL code within Snowflake, whereas Java UDFs require uploading a JAR file containing the compiled Java code.
Answer: B,E
Explanation:
SQL UDFs are suitable for simpler transformations within Snowflake and cannot make external calls. They are defined directly using SQL code. Java UDFs, on the other hand, offer more flexibility by allowing complex logic implementation, interaction with external services/libraries via JAR files, and custom code. Java UDFs are generally perform better when complex transformations are needed, where SQL UDFs can become cumbersome. Performance depends on the workload. Option B is wrong becuase SQL UDFs are more performant for simpler tasks. Option D is wrong becuase its highly dependant on workload, where options E is wrong as Java UDFs are very useful and not deprecated.

NEW QUESTION # 77
You are designing a Snowflake data pipeline that continuously ingests clickstream dat a. You need to monitor the pipeline for latency and throughput, and trigger notifications if these metrics fall outside acceptable ranges. Which of the following combinations of Snowflake features and techniques would be MOST effective for achieving this goal?
  • A. Use Snowflake's 'QUERY_HISTORY view to track query execution times and implement a scheduled task that queries this view, calculates latency and throughput, and sends email notifications using Snowflake's built-in email integration if thresholds are exceeded.
  • B. Use Snowflake's Event Tables and Event Notifications to capture events related to data ingestion and processing. Configure alerts based on event patterns that indicate latency or throughput issues.
  • C. Implement a combination of Snowflake Streams, Tasks, and external functions. Streams capture changes, Tasks process the changes, and external functions send notifications to a monitoring service when latency or throughput issues are detected.
  • D. Rely on Snowflake's default resource monitors to track warehouse usage. If warehouse usage exceeds a certain threshold, assume there are performance issues and send a notification.
  • E. Create a custom dashboard using a Bl tool that connects to Snowflake via JDBC/ODBC and visualizes data ingestion and processing metrics. Manually monitor the dashboard for anomalies.
Answer: B,C
Explanation:
Options B and D offer the most effective solutions. Option B provides a granular approach using Streams, Tasks, and external functions for real-time monitoring and notification. Option D leverages Event Tables and Event Notifications, enabling a reactive approach based on specific event patterns. Option A is less precise as it relies on query history, which may not accurately reflect real-time latency. Option C is too general. Option E requires manual monitoring, which is not ideal for continuous pipelines.

NEW QUESTION # 78
You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?
  • A. Set the 'value.converter.schemas.enable' property to 'true'.
  • B. Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.
  • C. Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
  • D. Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.
  • E. Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
Answer: C
Explanation:
The correct answer is E. The 'org.apache.kafka.connect.transforms.Flatten' transformation is designed specifically for flattening nested JSON structures within Kafka Connect. By applying this transformation to the 'transforms' configuration of the Kafka Connector, you can instruct the connector to recursively flatten the nested JSON data before loading it into Snowflake, ensuring that all fields are accessible. Option A utilizes stages and is unrelated to nested structures. Option B enables schemas, which is useful for Avro, but does not inherently flatten nested structures. Option C allows for extraction but would require multiple transformations for each field and would be cumbersome. Option D will only load the full JSON but would not flatten it, failing the requirements.

NEW QUESTION # 79
......
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