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Databricks Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. Theuser_idfield represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table namedaccount_historywhich maintains a full record of all data in the same schema as the source. The next table in the system is namedaccount_currentand is implemented as a Type 1 table representing the most recent value for each uniqueuser_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the describedaccount_currenttable as part of each hourly batch job?

A. Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
B. Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
C. Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
D. Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.
E. Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

Answer: D

Explanation:
Explanation
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 116
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
B. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
C. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
E. A managed table will be created in the DBFS root storage container.

Answer: A

Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html

NEW QUESTION # 117
A table customerLocations exists with the following schema:
1. id STRING,
2. date STRING,
3. city STRING,
4. country STRING
A senior data engineer wants to create a new table from this table using the following command:
1. CREATE TABLE customersPerCountry AS
2. SELECT country,
3. COUNT(*) AS customers
4. FROM customerLocations
5. GROUP BY country;
A junior data engineer asks why the schema is not being declared for the new table. Which of the following
responses explains why declaring the schema is not necessary?

A. CREATE TABLE AS SELECT statements result in tables where schemas are optional
B. CREATE TABLE AS SELECT statements adopt schema details from the source table and query
C. CREATE TABLE AS SELECT statements infer the schema by scanning the data
D. CREATE TABLE AS SELECT statements assign all columns the type STRING
E. CREATE TABLE AS SELECT statements result in tables that do not support schemas

Answer: B

NEW QUESTION # 118
Which of the statements is correct when choosing between lakehouse and Datawarehouse?

A. Traditional Data warehouses have special indexes which are optimized for Machine learning
B. Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.
C. SQL support is only available for Traditional Datawarehouse's, Lakehouses support Python and Scala
D. Traditional Data warehouses can serve low query latency with high reliability for BI workloads
E. Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.

Answer: B

Explanation:
Explanation
The lakehouse replaces the current dependency on data lakes and data warehouses for modern data companies that desire:
* Open, direct access to data stored in standard data formats.
* Indexing protocols optimized for machine learning and data science.
* Low query latency and high reliability for BI and advanced analytics.

NEW QUESTION # 119
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?

A. Create a pull request
B. Delete branch
C. Approve the pull request
D. Commit and push code
E. Trigger Databricks CICD pipeline

Answer: D

Explanation:
Explanation
The answer is Commit and push code.
See the below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workflow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Exam focus: Please study the below image carefully to understand all of the steps in the CI/CD flow to understand the tasks that are implemented in Databricks Repo vs Git Provider, exam may ask a different type of questions based on this flow.
Diagram Description automatically generated

NEW QUESTION # 120
......

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