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Databricks Certified Data Engineer Professional Sample Questions:

1. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A) Total VMs: 2
200 GB per Executor
80 Cores / Executor
B) Total VMs: 4
100 GB per Executor
40 Cores/Executor
C) Total VMs: 8
50 GB per Executor
20 Cores / Executor
D) Total VMs: 1
400 GB per Executor
160 Cores / Executor

2. A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A) Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
B) COPY INTO SQL command with parameters to track processed files
C) Auto Loader with schema inference and evolution enabled
D) Traditional batch processing with scheduled Databricks Jobs

3. When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A) In the Spark UI, under the SQL/DataFrame tab
B) In the Query Profiler, under the Stages tab
C) In the Query Profiler, under Query Source
D) In the Spart UI, under the Jobs tab

4. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A) Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
B) Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
C) Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
D) Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.

5. A table in the Lakehouse named customer_churn_params is 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) 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.
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) 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.
D) 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.
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.

Solutions: