Real Databricks-Certified-Professional-Data-Engineer are Uploaded by BraindumpStudy provide 2025 Latest Databricks-Certified-Professional-Data-Engineer Practice Tests Dumps [Q11-Q26]

4.5/5 - (6 votes)

Real Databricks-Certified-Professional-Data-Engineer are Uploaded by BraindumpStudy provide 2025 Latest Databricks-Certified-Professional-Data-Engineer Practice Tests Dumps.

All Databricks-Certified-Professional-Data-Engineer Dumps and Databricks Certified Professional Data Engineer Exam Training Courses Help candidates to study and pass the Databricks Certified Professional Data Engineer Exam Exams hassle-free!

Databricks Certified Professional Data Engineer certification exam is designed for professionals who want to prove their expertise in designing, building, and maintaining data processing systems using Databricks. Databricks is a cloud-based data platform that provides a unified analytics engine for data processing, machine learning, and visualization. Databricks Certified Professional Data Engineer Exam certification exam tests the candidate’s knowledge and skills in various areas, including data engineering, data processing, data modeling, and data architecture.

QUESTION 11
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.

Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.

Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

QUESTION 12
A notebook accepts an input parameter that is assigned to a python variable called department and this is an optional parameter to the notebook, you are looking to control the flow of the code using this parameter. you have to check department variable is present then execute the code and if no department value is passed then skip the code execution. How do you achieve this using python?

1.if department is not None:
2. #Execute code
3.else:
4. pass
(Correct)

1.if (department is not None)
2. #Execute code
3.else
4. pass

1.if department is not None:
2. #Execute code
3.end:
4. pass

1.if department is not None:
2. #Execute code
3.then:
4. pass

1.if department is None:
2. #Execute code
3.else:
4. pass

QUESTION 13
Which of the following locations in the Databricks product architecture hosts the notebooks and jobs?

Data plane

Control plane

Databricks Filesystem

JDBC data source

Databricks web application

QUESTION 14
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited

Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1

QUESTION 15
Which of the following SQL statements can replace a python variable, when the notebook is set in SQL mode
1.table_name = “sales”
2.schema_name = “bronze”

spark.sql(f”SELECT * FROM f{schema_name.table_name}”)

spark.sql(f”SELECT * FROM {schem_name.table_name}”)

spark.sql(f”SELECT * FROM ${schema_name}.${table_name}”)

spark.sql(f”SELECT * FROM {schema_name}.{table_name}”)

spark.sql(“SELECT * FROM schema_name.table_name”)

QUESTION 16
Which of the statements are correct about lakehouse?

Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads

Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads

Lakehouse does not support ACID

In Lakehouse Storage and compute are coupled

Lakehouse supports schema enforcement and evolution

QUESTION 17
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?

DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.

By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.

Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

QUESTION 18
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

Data plane

Control plane

Databricks Filesystem

Databricks web application

JDBC data source

QUESTION 19
Which of the following scenarios is the best fit for the AUTO LOADER solution?

Efficiently process new data incrementally from cloud object storage

Incrementally process new streaming data from Apache Kafa into delta lake

Incrementally process new data from relational databases like MySQL

Efficiently copy data from data lake location to another data lake location

Efficiently move data incrementally from one delta table to another delta table

Explanation
The answer is, Efficiently process new data incrementally from cloud object storage.
Please note: AUTO LOADER only works on data/files located in cloud object storage like S3 or Azure Blob Storage it does not have the ability to read other data sources, although AU-TO LOADER is built on top of structured streaming it only supports files in the cloud object stor-age. If you want to use Apache Kafka then you can just use structured streaming.
Diagram Description automatically generated

Auto Loader and Cloud Storage Integration
Auto Loader supports a couple of ways to ingest data incrementally
1.Directory listing – List Directory and maintain the state in RocksDB, supports incremental file listing
2.File notification – Uses a trigger+queue to store the file notification which can be later used to retrieve the file, unlike Directory listing File notification can scale up to millions of files per day.
[OPTIONAL]
Auto Loader vs COPY INTO?
Auto Loader
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a new Structured Streaming source called cloudFiles. Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory.
When to use Auto Loader instead of the COPY INTO?
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*You do not plan to load subsets of previously uploaded files. With Auto Loader, it can be more difficult to reprocess subsets of files. However, you can use the COPY INTO SQL command to reload subsets of files while an Auto Loader stream is simultaneously running.
Refer to more documentation here,
https://docs.microsoft.com/en-us/azure/databricks/ingestion/auto-loader

QUESTION 20
Which of the following data workloads will utilize a gold table as its source?

A job that enriches data by parsing its timestamps into a human-readable format

A job that queries aggregated data that already feeds into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

A job that aggregates cleaned data to create standard summary statistics

A job that cleans data by removing malformatted records

QUESTION 21
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

Task queueing resulting from improper thread pool assignment.

Spill resulting from attached volume storage being too small.

Network latency due to some cluster nodes being in different regions from the source data

Skew caused by more data being assigned to a subset of spark-partitions.

Credential validation errors while pulling data from an external system.

QUESTION 22
Which of the following operations are not supported on a streaming dataset view?
spark.readStream.format(“delta”).table(“sales”).createOrReplaceTempView(“streaming_view”)

SELECT sum(unitssold) FROM streaming_view

SELECT max(unitssold) FROM streaming_view

SELECT id, sum(unitssold) FROM streaming_view GROUP BY id ORDER BY id

SELECT id, count(*) FROM streaming_view GROUP BY id

SELECT * FROM streadming_view ORDER BY id

QUESTION 23
You are still noticing slowness in query after performing optimize which helped you to resolve the small files problem, the column(transactionId) you are using to filter the data has high cardinality and auto incrementing number. Which delta optimization can you enable to filter data effectively based on this column?

Create BLOOM FLTER index on the transactionId

Perform Optimize with Zorder on transactionId
(Correct)

transactionId has high cardinality, you cannot enable any optimization.

Increase the cluster size and enable delta optimization

Increase the driver size and enable delta optimization

QUESTION 24
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION “dbfs:/mnt/sales/*.csv”

FORMAT CSV
OPTIONS ( “true”,”|”)

USING CSV
TYPE ( “true”,”|”)

USING CSV
OPTIONS ( header =”true”, delimiter = “|”)
(Correct)

FORMAT CSV
FORMAT TYPE ( header =”true”, delimiter = “|”)

FORMAT CSV
TYPE ( header =”true”, delimiter = “|”)

QUESTION 25
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?

Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.

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.

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.

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.

QUESTION 26
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 invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints 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?

Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

The activity details table already exists; CHECK constraints can only be added during initial table creation.

The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

The aim of the Databricks-Certified-Professional-Data-Engineer certification is to create a standard for data engineering skills in the big data industry. Databricks Certified Professional Data Engineer Exam certification demonstrates that professionals have the knowledge and skills needed to work effectively on complex big data projects in the cloud. It also improves the candidate’s chances of getting hired, retaining their job, or earning a promotion in a highly competitive industry.

Valid Way To Pass Databricks’s Databricks-Certified-Professional-Data-Engineer Exam with : https://www.braindumpstudy.com/Databricks-Certified-Professional-Data-Engineer_braindumps.html