Free Access Databricks.Databricks-Certified-Professional-Data-Engineer.v2024-03-27.q76 Practice Test (Page 5)

Question 16

Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?

A.1. (spark.table("sales")
2. .withColumn("avgPrice", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("append")
6. .table("cleanedSales")
7.)

B.1. (spark.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.)

C.1. (spark.table("sales")
2. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. )

D.1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )

E.1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )

Question 17

A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A.Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

B.Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

C.Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.

D.Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.

E.Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

Question 18

The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table namedusers.

Assuming thatuser_idis a unique identifying key and that contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A.Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.

B.No; the Delta cache may return records from previous versions of the table until the cluster is restarted.

C.Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.

D.No; the Delta Lake delete command only provides ACID guarantees when combined with the mergeinto command.

E.No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Question 19

A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A.Set the configuration delta.deduplicate = true.

B.VACUUM the Delta table after each batch completes.

C.Perform an insert-only merge with a matching condition on a unique key.

D.Perform a full outer join on a unique key and overwrite existing data.

E.Rely on Delta Lake schema enforcement to prevent duplicate records.

Question 20

What are the different ways you can schedule a job in Databricks workspace?

A.Continuous, Incremental

B.On-Demand runs, File notification from Cloud object storage

C.Cron, On Demand runs

D.Cron, File notification from Cloud object storage

E.Once, Continuous

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Question 16

Question 17

Question 18

Question 19

Question 20

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