Updated PDF (New 2023) Actual Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions [Q30-Q52]

4/5 - (2 votes)

Updated PDF (New 2023) Actual Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions

Verified Databricks-Certified-Professional-Data-Engineer Exam Dumps PDF [2023] Access using Real4exams

Databricks Certified Professional Data Engineer exam measures a candidate’s ability to design, build, and manage data pipelines using Databricks. It covers a wide range of topics, including data ingestion, transformation, storage, and analysis. Candidates must demonstrate their proficiency in using Databricks tools and techniques to solve real-world data engineering problems. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers who want to validate their skills and expertise in using Databricks to build and manage data pipelines.

Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.

NO.30 When using the complete mode to write stream data, how does it impact the target table?

Entire stream waits for complete data to write

Stream must complete to write the data

Target table cannot be updated while stream is pending

Target table is overwritten for each batch

Delta commits transaction once the stream is stopped

NO.31 What is the purpose of a gold layer in Multi-hop architecture?

Optimizes ETL throughput and analytic query performance

Eliminate duplicate records

Preserves grain of original data, without any aggregations

Data quality checks and schema enforcement

Powers ML applications, reporting, dashboards and adhoc reports.

NO.32 If you create a database sample_db with the statement CREATE DATABASE sample_db what will be the default location of the database in DBFS?

Default location, DBFS:/user/

Default location, /user/db/

Default Storage account

Statement fails “Unable to create database without location”

Default Location, dbfs:/user/hive/warehouse

NO.33 A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?

They can set up a retry policy on the first Job to help it run more quickly

They can use cluster pools to help the Jobs run more efficiently

They can limit the size of the output in the second Job so that it will not fail as easily

They can set up the data to stream from the first Job to the second Job

They can utilize multiple tasks in a single job with a linear dependency

NO.34 Which of the following is true, when building a Databricks SQL dashboard?

A dashboard can only use results from one query

Only one visualization can be developed with one query result

A dashboard can only connect to one schema/Database

More than one visualization can be developed using a single query result

A dashboard can only have one refresh schedule

NO.35 You are currently working with the second team and both teams are looking to modify the same notebook, you noticed that the second member is copying the notebooks to the personal folder to edit and replace the collaboration notebook, which notebook feature do you recommend to make the process easier to collaborate.

Databricks notebooks should be copied to a local machine and setup source control lo-cally to version the notebooks

Databricks notebooks support automatic change tracking and versioning

Databricks Notebooks support real-time coauthoring on a single notebook

Databricks notebooks can be exported into dbc archive files and stored in data lake

Databricks notebook can be exported as HTML and imported at a later time

NO.36 A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format(“cloudFiles”)
2. .option(“cloudFiles.format”, “json”)
3. .option(“cloudFiles.schemaLocation”, schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?

There is no change required. The inclusion of format(“cloudFiles”) enables the use of Auto Loader

There is no change required. Databricks automatically uses Auto Loader for streaming reads

The data engineer needs to change the format(“cloudFiles”) line to format(“autoLoader”)

The data engineer needs to add the .autoLoader line before the .load(sourcePath) line

There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader

NO.37 You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

AS DEFAULT (CAST(orderTime as DATE))

GENERATED ALWAYS AS (CAST(orderTime as DATE))
Correct)

GENERATED DEFAULT AS (CAST(orderTime as DATE))

AS (CAST(orderTime as DATE))

Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process

NO.38 A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > ‘2020-01-01’) What is the expected behavior when a batch of data containing data that violates these constraints is processed?

Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Records that violate the expectation cause the job to fail.

Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.

Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

NO.39 What is the underlying technology that makes the Auto Loader work?

Loader

Delta Live Tables

Structured Streaming

DataFrames

Live DataFames

NO.40 Data engineering team is required to share the data with Data science team and both the teams are using different workspaces in the same organizationwhich of the following techniques can be used to simplify sharing data across?
*Please note the question is asking how data is shared within an organization across multiple workspaces.

Data Sharing

Unity Catalog

DELTA lake

Use a single storage location

DELTA LIVE Pipelines

NO.41 The current ELT pipeline is receiving data from the operations team once a day so you had setup an AUTO LOADER process to run once a day using trigger (Once = True) and scheduled a job to run once a day, operations team recently rolled out a new feature that allows them to send data every 1 min, what changes do you need to make to AUTO LOADER to process the data every 1 min.

Convert AUTO LOADER to structured streaming

Change AUTO LOADER trigger to .trigger(ProcessingTime = “1 minute”)

Setup a job cluster run the notebook once a minute

Enable stream processing

Change AUTO LOADER trigger to (“1 minute”)

NO.42 You had AUTO LOADER to process millions of files a day and noticed slowness in load process, so you scaled up the Databricks cluster but realized the performance of the Auto loader is still not improving, what is the best way to resolve this.

AUTO LOADER is not suitable to process millions of files a day

Setup a second AUTO LOADER process to process the data

Increase the maxFilesPerTrigger option to a sufficiently high number

Copy the data from cloud storage to local disk on the cluster for faster access

Merge files to one large file

NO.43 You are working to set up two notebooks to run on a schedule, the second notebook is dependent on the first notebook but both notebooks need different types of compute to run in an optimal fashion, what is the best way to set up these notebooks as jobs?

Use DELTA LIVE PIPELINES instead of notebook tasks

A Job can only use single cluster, setup job for each notebook and use job dependency to link both jobs together

Each task can use different cluster, add these two notebooks as two tasks in a single job with linear dependency and modify the cluster as needed for each of the tasks

Use a single job to setup both notebooks as individual tasks, but use the cluster API to setup the second cluster before the start of second task

Use a very large cluster to run both the tasks in a single job

NO.44 Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql(“select count(*) from table”).collect()[0][0]

assert (row_count = 10, “Row count did not match”)

assert if (row_count = 10, “Row count did not match”)

assert row_count == 10, “Row count did not match”

assert if row_count == 10, “Row count did not match”

assert row_count = 10, “Row count did not match”

NO.45 Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

NO.46 Which of the following tool provides Data Access control, Access Audit, Data Lineage, and Data discovery?

DELTA LIVE Pipelines

Unity Catalog

Data Governance

DELTA lake

Lakehouse

NO.47 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.

NO.48 How do you handle failures gracefully when writing code in Pyspark, fill in the blanks to complete the below statement
1._____
2.
3. Spark.read.table(“table_name”).select(“column”).write.mode(“append”).SaveAsTable(“new_table_name”)
4.
5._____
6.
7. print(f”query failed”)

try: failure:

try: catch:

try: except:

try: fail:

try: error:

NO.49 You are working on a dashboard that takes a long time to load in the browser, due to the fact that each visualization contains a lot of data to populate, which of the following approaches can be taken to address this issue?

Increase size of the SQL endpoint cluster

Increase the scale of maximum range of SQL endpoint cluster

Use Databricks SQL Query filter to limit the amount of data in each visualization

Remove data from Delta Lake

Use Delta cache to store the intermediate results

NO.50 Which of the following SQL statements can replace python variables in Databricks SQL code, when the notebook is set in SQL mode?
1.%python
2.table_name = “sales”
3.schema_name = “bronze”
4.
5.%sql
6.SELECT * FROM ____________________

SELECT * FROM f{schema_name.table_name}

SELECT * FROM {schem_name.table_name}

SELECT * FROM ${schema_name}.${table_name}

SELECT * FROM schema_name.table_name

NO.51 A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?

A data lakehouse requires very little data modeling

A data lakehouse combines compute and storage for simple governance

A data lakehouse fully exists in the cloud

A data lakehouse stores unstructured data and is ACID-compliant

A data lakehouse provides autoscaling for compute clusters

NO.52 Unity catalog helps you manage the below resources in Databricks at account level

Tables

ML Models

Dashboards

Meta Stores and Catalogs

All of the above