Databricks-Certified-Professional-Data-Engineer Archives

QUESTION 35
A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?

CREATE DATABASE customer360 DELTA LOCATION ‘/customer/customer360’;

CREATE DATABASE customer360 LOCATION ‘/customer/customer360’;

CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION ‘/customer/customer360’;

CREATE DATABASE IF NOT EXISTS customer360;

CREATE DATABASE IF NOT EXISTS customer360 LOCATION ‘/customer/customer360’;

QUESTION 36
Which of the following SQL commands are used to append rows to an existing delta table?

APPEND INTO DELTA table_name

APPEND INTO table_name

COPY DELTA INTO table_name

INSERT INTO table_name

UPDATE table_name

QUESTION 37
What is the main difference between the bronze layer and silver layer in a medallion architecture?

Duplicates are removed in bronze, schema is applied in silver

Silver may contain aggregated data

Bronze is raw copy of ingested data, silver contains data with production schema and optimized for ELT/ETL throughput

Bad data is filtered in Bronze, silver is a copy of bronze data

QUESTION 38
Which of the statements are incorrect when choosing between lakehouse and Datawarehouse?

Lakehouse can have special indexes and caching which are optimized for Machine learning

Lakehouse cannot serve low query latency with high reliability for BI workloads, only suitable for batch workloads.

Lakehouse can be accessed through various API’s including but not limited to Py-thon/R/SQL

Traditional Data warehouses have storage and compute are coupled.

Lakehouse uses standard data formats like Parquet.

QUESTION 39
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.

QUESTION 40
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

Three columns will be returned, but one column will be named “redacted” and contain only null values.

Only the email and itv columns will be returned; the email column will contain all null values.

The email and ltv columns will be returned with the values in user itv.

The email, age. and ltv columns will be returned with the values in user ltv.

Only the email and ltv columns will be returned; the email column will contain the string
“REDACTED” in each row.

QUESTION 41
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?

P(E1,E2,E3)P(E1)/P(E2:E3)

P(E1,E2;E3)/P(E2,E3)

P(E1,E2|E3)P(E2|E3)P(E3)

P(E1,E2|E3)P(E3)

P(E1,E2,E3)P(E2)P(E3)

QUESTION 42
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

Data plane

Control plane

Databricks Filesystem

JDBC data source

Databricks web application

QUESTION 43
You are trying to create an object by joining two tables that and it is accessible to data scientist’s team, so it does not get dropped if the cluster restarts or if the notebook is detached. What type of object are you trying to create?

Temporary view

Global Temporary view

Global Temporary view with cache option

External view

View

QUESTION 44
Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to
estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total
number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to
estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit
rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash
conclusions.

Naive Bayes

Laplace Smoothing

Logistic Regression

Linear Regression

QUESTION 45
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

DELTA LIVE TABLES do not support job cluster

Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook

Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file

Use Pipeline creation UI, select a new pipeline and job cluster

QUESTION 46
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table(“sales”)
2. .withColumn(“avg_price”, col(“sales”) / col(“units”))
3. .writeStream
4. .option(“checkpointLocation”, checkpointPath)
5. .outputMode(“complete”)
6. ._____
7. .table(“new_sales”)
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?

.processingTime(1)

.processingTime(“once”)

.trigger(processingTime=”once”)

.trigger(once=True)

.trigger(continuous=”once”)

QUESTION 47
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.

QUESTION 48
A team member is leaving the team and he/she is currently the owner of the few tables, instead of transfering the ownership to a user you have decided to transfer the ownership to a group so in the future anyone in the group can manage the permissions rather than a single individual, which of the following commands help you accomplish this?

ALTER TABLE table_name OWNER to ‘group’

TRANSFER OWNER table_name to ‘group’

GRANT OWNER table_name to ‘group’*

ALTER OWNER ON table_name to ‘group’

GRANT OWNER On table_name to ‘group’

QUESTION 49
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year’s worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?

Linear regression

Logistic regression

Decision trees

TF-IDF

QUESTION 50
While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?

Review event logs in the Workspace

Run SQL SHOW HISTORY table_name

Check Databricks SQL Audit logs

Run SQL command DESCRIBE HISTORY table_name

Review workspace audit logs

QUESTION 51
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

Discovery

Data Preparation

Model Building

Communicate Results

QUESTION 52
What is the purpose of a silver layer in Multi hop architecture?

Replaces a traditional data lake

Efficient storage and querying of full and unprocessed history of data

A schema is enforced, with data quality checks.

Refined views with aggregated data

Optimized query performance for business-critical data

QUESTION 53
Which of the following scenarios is the best fit for AUTO LOADER?

Efficiently process new data incrementally from cloud object storage

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

Incrementally process new data from streaming data sources like Kafka into delta lake

Incrementally process new data from relational databases like MySQL

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

QUESTION 54
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.

QUESTION 55
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

QUESTION 56
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

QUESTION 57
Which statement regarding stream-static joins and static Delta tables is correct?

Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job’s initialization.

The checkpoint directory will be used to track state information for the unique keys present in the join.

Stream-static joins cannot use static Delta tables because of consistency issues.

The checkpoint directory will be used to track updates to the static Delta table.

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Category Archives: Databricks-Certified-Professional-Data-Engineer

2024 Latest Databricks-Certified-Professional-Data-Engineer dumps Exam Material with 60 Questions [Q35-Q57]

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