Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Exam Dumps Preview

A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing.

How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units.

How should the platform lead generate an automated report that can be shared daily?

A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?

A Delta Lake table was created with the below query:

Consider the following query:

DROP TABLE prod.sales_by_store If this statement is executed by a workspace admin, which result will occur?

The business intelligence team has a dashboard configured to track various summary metrics for retail stores. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table, named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.

Which solution meets the expectations of the end users while controlling and limiting possible costs?

A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query.

Which SQL query will accurately return the daily usage by product?

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.

A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.

Which statement explains the cause of this failure?

The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API. They need to list all jobs, identify a failed job, and rerun it.

Which sequence of API actions should the data engineer perform?

A data engineer is optimizing a managed table that suffers from data skew and frequently changing query filter columns. The engineer needs to avoid costly data rewrites when query patterns evolve. The table size is under 1TB.

How should the data engineer meet this requirement?

A data engineer has a delta table order with deletion vectors enabled for it. The engineer is attempting to execute the below code:

DELETE FROM orders WHERE status = 'cancelled'

What should be the behaviour of deletion vectors when the command is executed?

A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.

Which authentication approach will provide the highest level of security?

An organization processes customer data from web and mobile applications. Data includes names, emails phone numbers, and location history. Data arrives both as Batch files from an SFTP drop (daily) and Streaming JSON events from Kafka (real-time).

To comply with internal data privacy policies, the following requirements must be met: Personally identifiable information (PII) like email, phone_number, and ip_address must be masked or anonymized before storage Both batch and streaming pipelines must apply consistent PII handling Masking logic must be auditable and reproducible.
The masked data must still be usable for downstream analytics.

How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability of transformations for audits?

A data engineer us ingesting JSON files from cloud object storage using Databricks Auto Loader. The source folder may occasionally receive large files of data, which risks overwhelming the stream. To ensure predictable micro-batch sizes, the team wants to throttle ingestion based on the volume of data scanned at 1 GB, regardless of the number of files.

Which Auto Loader configuration should the data engineer used to achieve this?

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.

The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.

Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.

The data engineer already has this code:

How should the data engineer meet the requirements to capture good and bad data?

A view is registered with the following code:

Both users and orders are Delta Lake tables.

Which statement describes the results of querying recent_orders?

A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access).

Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?

A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table. The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values. The team wants to trigger an alert when the daily average of percent_null exceeds 5% for three consecutive days, while ensuring notifications aren't spammed during sustained issues.

Which SQL alert configuration achieves this goal while minimizing false positives and redundant notifications?

A data engineer is building a Lakeflow Spark Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:

The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.

How should the data engineer achieve this?

A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%.

Which SQL query pattern is appropriate for implementing this multi-column alert condition?

A data engineer is using Lakeflow Spark Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement - the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.

This is the existing code for their silver table:

Which code will satisfy the requirements?

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.

The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.

Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays.

How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.

Which code block accomplishes this task while minimizing potential compute costs?

A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data.

Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling.

Which approach should identify the specific bottlenecks using native Databricks tools?

Given the following PySpark code snippet in a Databricks notebook:

filtered_df=spark.read.format("delta").load("/mnt/daya/large_table") \ .filter ("event_date> '2024-01-01'")
filtered_df.count ()

The data engineer notices from the Query Profile that the scan operator for filtered_df is reading almost all files, despite a filter being applied.

What is the probable reason for poor data skipping?

A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.

How can the data engineer fix this?

A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are: Grant the data-engineers group CAN_MANAGE access to the job. Ensure the auditors' group can view the job but not modify/run it. Avoid granting unintended permissions to other users/groups.

How should the data engineer deploy the job while meeting the requirements?

Which Python variable contains a list of directories to be searched when trying to locate required modules?

A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.

Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy.

Which code snippet correctly sets this retention period for deleted files?

The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance.

Which Delta Lake feature should they enable?

Review the following error traceback:

Which statement describes the error being raised?

What is the first of a Databricks Python notebook when viewed in a text editor?

Which statement regarding stream-static joins and static Delta tables is correct?

Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().

Which of the following statements is correct?