[Full-Version] 2025 New Databricks-Certified-Data-Engineer-Professional Actual Exam Dumps, Databricks Practice Test
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NEW QUESTION # 64
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from made based upon what is most convenient for the workspace administrator.
- B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- D. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- E. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
Answer: E
Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.
NEW QUESTION # 65
Which of the following is true of Delta Lake and the Lakehouse?
- A. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
- B. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
- C. Z-order can only be applied to numeric values stored in Delta Lake tables
- D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
- E. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
Answer: A
Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.
NEW QUESTION # 66
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
- A. Bytes Received never exceeds 80 million bytes per second
- B. The five Minute Load Average remains consistent/flat
- C. Network I/O never spikes
- D. CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
- E. Total Disk Space remains constant
Answer: D
Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.
NEW QUESTION # 67
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?
- A. Manual
- B. Unit
- C. Integration
- D. functional
- E. End-to-end
Answer: B
Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
NEW QUESTION # 68
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. Cluster creation allowed. "Can Attach To" privileges on the required cluster
- B. "Can Restart" privileges on the required cluster
- C. Cluster creation allowed. "Can Restart" privileges on the required cluster
- D. "Can Manage" privileges on the required cluster
- E. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
Answer: B
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
NEW QUESTION # 69
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. awaitArrival("event_time", "10 minutes")
- B. delayWrite("event_time", "10 minutes")
- C. withWatermark("event_time", "10 minutes")
- D. slidingWindow("event_time", "10 minutes")
- E. await("event_time + `10 minutes'")
Answer: C
Explanation:
This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming.
NEW QUESTION # 70
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?
- A. The total count of rows is calculated by scanning all data files
- B. The total count of records is calculated from the Hive metastore
- C. The total count of records is calculated from the parquet file metadata
- D. The total count of rows will be returned from cached results unless REFRESH is run
- E. The total count of records is calculated from the Delta transaction logs
Answer: E
Explanation:
Delta Lake maintains a transaction log that records details about every change made to a table.
When you execute a count operation on a Delta table, Delta Lake can use the information in the transaction log to calculate the total number of records without having to scan all the data files.
This is because the transaction log includes information about the number of records in each file, allowing for an efficient aggregation of these counts to get the total number of records in the table.
NEW QUESTION # 71
The data engineering team maintains the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?
- A. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
- B. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
- C. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
- D. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
- E. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
Answer: B
Explanation:
This is the correct answer because it describes what will occur when this code is executed. The Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables.
NEW QUESTION # 72
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?
- A. When a database is being created, make sure that the LOCATION keyword is used.
- B. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
- C. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
- D. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
- E. When the workspace is being configured, make sure that external cloud object storage has been mounted.
Answer: D
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
NEW QUESTION # 73
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
- A. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
- B. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
- C. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- D. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
- E. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
Answer: C
Explanation:
Databricks jobs create will create a new job with the same name each time it is run.
In order to overwrite the extsting job you need to run databricks jobs reset
NEW QUESTION # 74
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?
- A. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
- B. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
- C. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
- D. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
- E. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
Answer: C
Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.
NEW QUESTION # 75
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
- A. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
- B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
- C. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
- D. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
- E. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
Answer: C
Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table.
NEW QUESTION # 76
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?
- A. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
- B. Instruct the marketing team to download results as a CSV and email them to the sales organization.
- C. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
- D. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
- E. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
Answer: C
Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
NEW QUESTION # 77
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?
- A. ZORDER ON review will need to be run to see performance gains.
- B. The Delta log creates a term matrix for free text fields to support selective filtering.
- C. Text data cannot be stored with Delta Lake.
- D. Delta Lake statistics are only collected on the first 4 columns in a table.
- E. Delta Lake statistics are not optimized for free text fields with high cardinality.
Answer: E
Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data.
NEW QUESTION # 78
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
- A. No; each of the streams needs to have its own checkpoint directory.
- B. Yes; both of the streams can share a single checkpoint directory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from - C. No; only one stream can write to a Delta Lake table.
- D. Yes; Delta Lake supports infinite concurrent writers.
- E. No; Delta Lake manages streaming checkpoints in the transaction log.
Answer: A
Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss.
NEW QUESTION # 79
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.
- A. Use Repos to merge all differences and make a pull request back to the remote repository.
- B. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
- C. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
- D. Use repos to merge all difference and make a pull request back to the remote repository.
- E. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
Answer: E
Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.
NEW QUESTION # 80
What is the first of a Databricks Python notebook when viewed in a text editor?
- A. # Databricks notebook source
- B. %python
- C. // Databricks notebook source
- D. # MAGIC %python
- E. -- Databricks notebook source
Answer: A
Explanation:
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook
NEW QUESTION # 81
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
- A. Can manage
- B. Can run
- C. Can Read
- D. Can edit
Answer: C
Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.
NEW QUESTION # 82
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
- A. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
- B. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
- C. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
- D. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
- E. Merge all changes back to the main branch in the remote Git repository and clone the repo again
Answer: D
Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.
NEW QUESTION # 83
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