Updated PDF (New 2023) Actual Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions [Q20-Q38]

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

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NO.20 you are currently working on creating a spark stream process to read and write in for a one-time micro batch, and also rewrite the existing target table, fill in the blanks to complete the below command sucesfully.
1.spark.table(“source_table”)
2..writeStream
3..option(“____”, “dbfs:/location/silver”)
4..outputMode(“____”)
5..trigger(Once=____)
6..table(“target_table”)

checkpointlocation, complete, True

targetlocation, overwrite, True

checkpointlocation, True, overwrite

checkpointlocation, True, complete

checkpointlocation, overwrite, True

NO.21 Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?

Merge when code is committed

Pull request and review process

Trigger Databricks Repos API to pull the latest version of code into production folder

Resolve merge conflicts

Delete a branch

NO.22 A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > ‘2020-01-01’) ON VIOLATION DROP ROW 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.23 When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

UNCACHE TABLE table_name

CACHE TABLE table_name

REFRESH TABLE table_name

BROADCAST TABLE table_name

CLEAR CACH table_name

NO.24 John Smith is a newly joined team member in the Marketing team who currently has access read access to sales tables but does not have access to delete rows from the table, which of the following commands help you accomplish this?

GRANT USAGE ON TABLE table_name TO [email protected]

GRANT DELETE ON TABLE table_name TO [email protected]

GRANT DELETE TO TABLE table_name ON [email protected]

GRANT MODIFY TO TABLE table_name ON [email protected]

GRANT MODIFY ON TABLE table_name TO [email protected]

NO.25 A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

The tables should be updated before the next query is run

The tables should be converted to the Delta format

The tables should be refreshed in the writing cluster before the next query is run

The tables should be altered to include metadata to not cache

The tables should be stored in a cloud-based external system

NO.26 What is the underlying technology that makes the Auto Loader work?

Loader

Delta Live Tables

Structured Streaming

DataFrames

Live DataFames

NO.27 What is the best way to describe a data lakehouse compared to a data warehouse?

A data lakehouse provides a relational system of data management

A data lakehouse captures snapshots of data for version control purposes.

A data lakehouse couples storage and compute for complete control.

A data lakehouse utilizes proprietary storage formats for data.

A data lakehouse enables both batch and streaming analytics.

Explanation
Anser is A data lakehouse enables both batch and streaming analytics.
A lakehouse has the following key features:
*Transaction support: In an enterprise lakehouse many data pipelines will often be reading and writing data concurrently. Support for ACID transactions ensures consistency as multi-ple parties concurrently read or write data, typically using SQL.
*Schema enforcement and governance: The Lakehouse should have a way to support schema enforcement and evolution, supporting DW schema architectures such as star/snowflake-schemas. The system should be able to reason about data integrity, and it should have robust governance and auditing mechanisms.
*BI support: Lakehouses enable using BI tools directly on the source data. This reduces staleness and improves recency, reduces latency, and lowers the cost of having to operationalize two copies of the data in both a data lake and a warehouse.
*Storage is decoupled from compute: In practice this means storage and compute use sepa-rate clusters, thus these systems are able to scale to many more concurrent users and larger data sizes. Some modern data warehouses also have this property.
*Openness: The storage formats they use are open and standardized, such as Parquet, and they provide an API so a variety of tools and engines, including machine learning and Py-thon/R libraries, can efficiently access the data directly.
*Support for diverse data types ranging from unstructured to structured data: The lakehouse can be used to store, refine, analyze, and access data types needed for many new data applications, including images, video, audio, semi-structured data, and text.
*Support for diverse workloads: including data science, machine learning, and SQL and analytics. Multiple tools might be needed to support all these workloads but they all rely on the same data repository.
*End-to-end streaming: Real-time reports are the norm in many enterprises. Support for streaming eliminates the need for separate systems dedicated to serving real-time data applications.

NO.28 Which of the following data workloads will utilize a Bronze table as its destination?

A job that aggregates cleaned data to create standard summary statistics

A job that queries aggregated data to publish key insights into a dashboard

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

A job that develops a feature set for a machine learning application

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

NO.29 Which of the statements is correct when choosing between lakehouse and Datawarehouse?

Traditional Data warehouses have special indexes which are optimized for Machine learning

Traditional Data warehouses can serve low query latency with high reliability for BI workloads

SQL support is only available for Traditional Datawarehouse’s, Lakehouses support Python and Scala

Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.

Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.

NO.30 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.31 What type of table is created when you create delta table with below command?
CREATE TABLE transactions USING DELTA LOCATION “DBFS:/mnt/bronze/transactions”

Managed delta table

External table

Managed table

Temp table

Delta Lake table

NO.32 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

NO.33 You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

AUTO LOADER

JOBS and TASKS

SQL Endpoints

DELTA LIVE TABLES

STRUCTURED STREAMING with MULTI HOP

NO.34 if you run the command VACUUM transactions retain 0 hours? What is the outcome of this command?

Command will be successful, but no data is removed

Command will fail if you have an active transaction running

Command will fail, you cannot run the command with retentionDurationcheck enabled

Command will be successful, but historical data will be removed

Command runs successful and compacts all of the data in the table

NO.35 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.36 You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

Setup an additional job to run ahead of the actual job so the cluster is running second job starts

Use the Databricks cluster pools feature to reduce the startup time

Use Databricks Premium edition instead of Databricks standard edition

Pin the cluster in the cluster UI page so it is always available to the jobs

Disable auto termination so the cluster is always running

NO.37 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.38 Which of the following table constraints that can be enforced on Delta lake tables are supported?

Primary key, foreign key, Not Null, Check Constraints

Primary key, Not Null, Check Constraints

Default, Not Null, Check Constraints

Not Null, Check Constraints

Unique, Not Null, Check Constraints

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Updated PDF (New 2023) Actual Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions [Q20-Q38]

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