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

Section	Weight	Objectives
Data Processing	28%	- ETL Pipelines - Data Transformation - Structured Streaming - Spark SQL
Databricks Lakehouse Platform	24%	- Delta Lake - Unity Catalog - Lakehouse Architecture - Data Management
Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Data Quality and Governance	12%	- Governance - Data Quality - Data Lineage
Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q17-Q22):

問題 #17
A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

A. Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.
B. Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.
C. Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
D. Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.

答案：A

解題說明：
Implementing quarantine logic allows invalid records to be isolated for further analysis while keeping them out of trusted datasets. Fixing the pipeline and re-running it ensures the expectation continues to enforce data quality standards, prevents future failures, and enables root-cause analysis of the faulty sensor data without compromising downstream consumers.

問題 #18
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
B. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
C. Deduplicate records in each batch by pk_id and overwrite the target table.
D. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

答案：D

問題 #19
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

A. Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.
B. Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
C. Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
D. Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
E. Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

答案：E

解題說明：
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column.

問題 #20
A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
B. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
C. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
D. Use the "Validate" option in the notebook to check for syntax errors.

答案：D

解題說明：
Databricks provides a "Validate" option within the Lakeflow Declarative Pipeline development interface that checks pipeline configurations, transformations, and syntax errors before actual execution.
This feature parses and validates the pipeline logic defined in notebooks or workspace files to ensure correctness and consistency of table dependencies, DLT (Delta Live Table) syntax, and schema references.
The validation process does not process or move any data, making it ideal for testing new configurations before deployment.
Using the shell terminal (B) or workspace files (D) does not perform integrated pipeline-level validation, while reconnecting to compute clusters (C) is unrelated to syntax checks. Therefore, the verified and correct approach is A.

問題 #21
The data science team has created and logged a production model 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, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

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

A. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
B. df.apply(model, columns).select("customer_id, predictions")
C. model.predict(df, columns)
D. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
E. df.select("customer_id", model(*columns).alias("predictions"))

答案：E

解題說明：
This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias("predictions") is used to name the output column of the model's predictions. This will result in a DataFrame with the desired schema: "customer_id LONG, predictions DOUBLE".

問題 #22
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