2022 Valid Databricks-Certified-Professional-Data-Engineer test answers & Databricks Exam PDF
Free Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions and Answer from Training Expert Exam4Tests
NEW QUESTION 23
Which of the following data workloads will utilize a Bronze table as its source?
- A. A job that ingests raw data from a streaming source into the Lakehouse
- B. A job that aggregates cleaned data to create standard summary statistics
- C. A job that enriches data by parsing its timestamps into a human-readable format
- D. A job that queries aggregated data to publish key insights into a dashboard
- E. A job that develops a feature set for a machine learning application
Answer: C
NEW QUESTION 24
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?
- A. .processingTime(1)
- B. .trigger(continuous="once")
- C. .processingTime("once")
- D. .trigger(once=True)
- E. .trigger(processingTime="once")
Answer: D
NEW QUESTION 25
Which of the following describes a scenario in which a data engineer will want to use a Job cluster instead of
an all-purpose cluster?
- A. A data team needs to collaborate on the development of a machine learning model
- B. An ad-hoc analytics report needs to be developed while minimizing compute costs
- C. A Databricks SQL query needs to be scheduled for upward reporting
- D. An automated workflow needs to be run every 30 minutes
- E. A data engineer needs to manually investigate a production error
Answer: D
NEW QUESTION 26
Projecting a multi-dimensional dataset onto which vector has the greatest variance?
- A. first eigenvector
- B. second eigenvector
- C. not enough information given to answer
- D. first principal component
- E. second principal component
Answer: D
Explanation:
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of
the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear
discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant
vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the
data onto the first principal component line, the data is more spread out (higher variance) than if projected onto
any other line, including other principal components.
NEW QUESTION 27
A data engineer has ingested a JSON file into a table raw_table with the following schema:
1.transaction_id STRING,
2.payload ARRAY<customer_id:STRING, date:TIMESTAMP, store_id:STRING>
The data engineer wants to efficiently extract the date of each transaction into a table with the fol-lowing
schema:
1.transaction_id STRING,
2.date TIMESTAMP
Which of the following commands should the data engineer run to complete this task?
- A. 1.SELECT transaction_id, date
2.FROM raw_table; - B. 1.SELECT transaction_id, explode(payload)
2.FROM raw_table; - C. 1.SELECT transaction_id, payload.date
2.FROM raw_table; - D. 1.SELECT transaction_id, payload[date]
2.FROM raw_table; - E. 1.SELECT transaction_id, date from payload
2.FROM raw_table;
Answer: C
NEW QUESTION 28
Which of the following statements describes Delta Lake?
- A. Delta Lake is an open source analytics engine used for big data workloads
- B. Delta Lake is an open format storage layer that delivers reliability, security, and per-formance
- C. Delta Lake is an open source data storage format for distributed data
- D. Delta Lake is an open source platform to help manage the complete machine learning lifecycle
- E. Delta Lake is an open format storage layer that processes data
Answer: B
Explanation:
Explanation
Delta Lake
NEW QUESTION 29
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?
- A. The tables should be altered to include metadata to not cache
- B. The tables should be updated before the next query is run
- C. The tables should be converted to the Delta format
- D. The tables should be stored in a cloud-based external system
- E. The tables should be refreshed in the writing cluster before the next query is run
Answer: C
NEW QUESTION 30
A new data engineer has started at a company. The data engineer has recently been added to the company's
Databricks workspace as [email protected]. The data engineer needs to be able to query the table
sales in the database retail. The new data engineer already has been granted USAGE on the database retail.
Which of the following commands can be used to grant the appropriate permissions to the new data engineer?
- A. GRANT USAGE ON TABLE sales TO [email protected];
- B. GRANT CREATE ON TABLE sales TO [email protected];
- C. GRANT USAGE ON TABLE [email protected] TO sales;
- D. GRANT SELECT ON TABLE sales TO [email protected];
- E. GRANT SELECT ON TABLE [email protected] TO sales;
Answer: D
NEW QUESTION 31
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum
likelihood estimate for the proportion of "pink" items in the test set of color balls?
- A. 2.4
- B. 24 0
- C. .24
- D. 4.8
- E. .48
Answer: C
Explanation:
Explanation
Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency
in the training set. The method of maximum likelihood corresponds to many well-known estimation methods
in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to
measure the height of every single penguin in a population due to cost or time constraints. Assuming that the
heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance
can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE
would accomplish this by taking the mean and variance as parameters and finding particular parametric values
that make the observed results the most probable (given the model).
In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects
the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes
the "agreement" of the selected model with the observed data, and for discrete random variables it indeed
maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood
estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution
and many other problems. However in some complicated problems, difficulties do occur: in such problems,
maximum-likelihood estimators are unsuitable or do not exist.
NEW QUESTION 32
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:
- A. Ridge and Lasso
- B. Integer programming
- C. Apriori Algorithm
- D. Ordinary Least squares
Answer: D
Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters
NEW QUESTION 33
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?
- A. Databricks Repos can store the single-source-of-truth Git repository
- B. Databricks Repos can commit or push code changes to trigger a CI/CD process
- C. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
- D. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
- E. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
Answer: B
NEW QUESTION 34
A data engineer wants to horizontally combine two tables as a part of a query. They want to use a shared
column as a key column, and they only want the query result to contain rows whose value in the key column is
present in both tables.
Which of the following SQL commands can they use to accomplish this task?
- A. LEFT JOIN
- B. INNER JOIN
- C. MERGE
- D. UNION
- E. OUTER JOIN
Answer: B
NEW QUESTION 35
A data engineer has created a Delta table as part of a data pipeline. Downstream data analysts now need
SELECT permission on the Delta table.
Assuming the data engineer is the Delta table owner, which part of the Databricks Lakehouse Plat-form can
the data engineer use to grant the data analysts the appropriate access?
- A. Repos
- B. Jobs
B Dashboards - C. Databricks Filesystem
- D. Data Explorer
Answer: A
NEW QUESTION 36
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. A data lakehouse provides autoscaling for compute clusters
- B. A data lakehouse requires very little data modeling
- C. A data lakehouse combines compute and storage for simple governance
- D. A data lakehouse fully exists in the cloud
- E. A data lakehouse stores unstructured data and is ACID-compliant
Answer: E
NEW QUESTION 37
A dataset has been defined using Delta Live Tables and includes an expectations clause:
1. CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01')
What is the expected behaviour when a batch of data containing data that violates these constraints is
processed?
- A. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset
- B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table
- C. Records that violate the expectation cause the job to fail
- D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log
- E. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log
Answer: D
NEW QUESTION 38
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order
for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this
process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta
Live Tables?
- A. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
- B. They need to create a Delta Live Tables pipeline from the Data page
- C. They need to create a Delta Live tables pipeline from the Compute page
- D. They need to create a Delta Live Tables pipeline from the Jobs page
- E. They need to refactor their notebook to use Python and the dlt library
Answer: D
NEW QUESTION 39
Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on
separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer
experienced in Databricks suggests there is a better alternative for this type of collaboration.
Which of the following supports the senior data engineer's claim?
- A. Databricks Notebooks support automatic change-tracking and versioning
- B. Databricks Notebooks support commenting and notification comments
- C. Databricks Notebooks support the creation of interactive data visualizations
- D. Databricks Notebooks support the use of multiple languages in the same notebook
- E. Databricks Notebooks support real-time co-authoring on a single notebook
Answer: E
NEW QUESTION 40
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?
- A. CREATE DATABASE IF NOT EXISTS customer360;
- B. CREATE DATABASE IF NOT EXISTS customer360 LOCATION '/customer/customer360';
- C. CREATE DATABASE customer360 DELTA LOCATION '/customer/customer360';
- D. CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION '/customer/customer360';
- E. CREATE DATABASE customer360 LOCATION '/customer/customer360';
Answer: B
NEW QUESTION 41
A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job's most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?
- A. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
than 60 - B. They can set up an Alert for the query to notify them if the returned value is greater than 60
- C. This type of alerting is not possible in Databricks
- D. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
minutes - E. They can set up an Alert for the query to notify when the ELT job fails
Answer: B
NEW QUESTION 42
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