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Databricks Certified Professional Data Engineer Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of
512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

  • A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
  • B. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
    1024*1024/512), and then write to parquet.
  • C. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
  • D. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
  • E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Answer: C

Explanation:
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly.
* Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of
512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
* Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
* Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB.
* The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).
References:
* Apache Spark Documentation: Configuration - spark.sql.files.maxPartitionBytes
* Databricks Documentation on Data Sources: Databricks Data Sources Guide


NEW QUESTION # 117
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

  • A. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
  • B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
  • C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
  • D. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

Answer: D

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
Reference: Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).


NEW QUESTION # 118
Data science team has requested they are missing a column in the table called average price, this can be calculated using units sold and sales amt, which of the following SQL statements allow you to reload the data with additional column

  • A. 1.INSERT OVERWRITE sales
    2.SELECT *, salesAmt/unitsSold as avgPrice FROM sales
  • B. 1.CREATE OR REPLACE TABLE sales
    2.AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales
  • C. OVERWRITE sales AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales
  • D. MERGE INTO sales USING (SELECT *, salesAmt/unitsSold as avgPrice FROM sales)
  • E. COPY INTO SALES AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales

Answer: B

Explanation:
Explanation
1.CREATE OR REPLACE TABLE sales
2.AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales
The main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to overwrite schema, only when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command will fail.


NEW QUESTION # 119
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

  • A. Always use X-small cluster
  • B. Reduce the size of the SQL Cluster size
  • C. Change the spot instance policy from reliability optimized to cost optimized
  • D. Reduce the max size of auto scaling from 10 to 5
  • E. Setup the dashboard refresh schedule to end in two weeks

Answer: E

Explanation:
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks


NEW QUESTION # 120
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

  • A. awaitArrival("event_time", "10 minutes")
  • B. slidingWindow("event_time", "10 minutes")
  • C. withWatermark("event_time", "10 minutes")
  • D. await("event_time + '10 minutes'")
  • E. delayWrite("event_time", "10 minutes")

Answer: C

Explanation:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html


NEW QUESTION # 121
......

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