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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Topic	Details
Topic 1	Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.
Topic 2	Data Processing: The topic covers understanding partition hints, partitioning data effectively, controlling part-file sizes, updating records, leveraging Structured Streaming and Delta Lake, implementing stream-static joins and deduplication. Additionally, it delves into utilizing Change Data Capture and addressing performance issues related to small files.
Topic 3	Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
What is the main difference between the bronze layer and silver layer in a medallion architecture?

A. Duplicates are removed in bronze, schema is applied in silver
B. Bad data is filtered in Bronze, silver is a copy of bronze data
C. Silver may contain aggregated data
D. Bronze is raw copy of ingested data, silver contains data with production schema and optimized for ELT/ETL throughput

Answer: D

Explanation:
Explanation
Medallion Architecture - Databricks
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 58
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. 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.
B. 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.
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. 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.
E. 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.

Answer: D

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.

NEW QUESTION # 59
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

A. Df.apply(model, columns). Select (''customer_id, prediction''
B. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')
C. Model, predict (df, columns)
D. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

Answer: C

Explanation:
Given the information that the model is registered with MLflow and assumingpredictis the method used to apply the model to a set of columns, we use themodel.predict()function to apply the model to the DataFrame dfusing the specifiedcolumns. Themodel.predict()function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictionscolumn.
When working with PySpark, this predictions column needs to be selected alongside thecustomer_idto create a new DataFrame with the schemacustomer_id LONG, predictions DOUBLE.
References:
* MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.
html#python-function-python
* PySpark MLlib documentation on model prediction:https://spark.apache.org/docs/latest/ml-pipeline.
html#pipeline

NEW QUESTION # 60
You are currently working on a production job failure with a job set up in job clusters due to a data issue, what cluster do you need to start to investigate and analyze the data?

A. A Job cluster can be used to analyze the problem
B. Existing job cluster can be used to investigate the issue
C. Databricks SQL Endpoint can be used to investigate the issue
D. All-purpose cluster/ interactive cluster is the recommended way to run commands and view the data.

Answer: D

Explanation:
Explanation
Answer is All-purpose cluster/ interactive cluster is the recommended way to run commands and view the data.
A job cluster can not provide a way for a user to interact with a notebook once the job is submitted, but an Interactive cluster allows to you display data, view visualizations write or edit quries, which makes it a perfect fit to investigate and analyze the data.

NEW QUESTION # 61
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 + Z-ORDER and implement a periodic OPTIMIZE command.
B. Alter the table to use 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 Hive-Style Partitions and implement a periodic OPTIMIZE command.

Answer: C

Explanation:
Databricks recommends Liquid Clustering for optimizing data layout in large Delta tables where query filters involve high-cardinality columns. Liquid Clustering automatically manages file organization and supports incremental maintenance without the need to rewrite data when clustering keys evolve. This is a key advantage over static partitioning or Z-ordering, which require costly file rewrites whenever optimization keys change. By combining Liquid Clustering with a periodic OPTIMIZE command, Databricks automatically compacts small files and maintains efficient data skipping performance. As stated in the Delta Lake optimization guide, Liquid Clustering is designed for scalability, minimal maintenance, and adaptability for analytical workloads with evolving query patterns-making B the correct answer.

NEW QUESTION # 62
......

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