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Real Databricks Databricks-Certified-Professional-Data-Engineer PDF Questions [2025]-Secret To Pass Exam In First Attempt

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q196-Q201):

NEW QUESTION # 196
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. All records are cached to attached storage and then the filter is applied
B. The Hive metastore is scanned for min and max statistics for the latitude column
C. The Parquet file footers are scanned for min and max statistics for the latitude column
D. All records are cached to an operational database and then the filter is applied
E. The Delta log is scanned for min and max statistics for the latitude column

Answer: E

Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column

NEW QUESTION # 197
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
C. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

Answer: C

Explanation:
Explanation
This is the best configuration for scheduling Structured Streaming jobs for production, as it automatically recovers from query failures and keeps costs low. A new job cluster is created for each run of the job and terminated when the job completes, which saves costs and avoids resource contention. Retries are not needed for Structured Streaming jobs, as they can automatically recover from failures using checkpointing and write-ahead logs. Maximum concurrent runs should be set to 1 to avoid duplicate output or data loss. Verified References: Databricks Certified Data Engineer Professional, under "Monitoring & Logging" section; Databricks Documentation, under "Schedule streaming jobs" section.

NEW QUESTION # 198
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
C. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
E. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.

Answer: A

Explanation:
Explanation
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed. This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table12. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.
The other options are not as simple or efficient as the proposed approach, because:
Option A would require applying the churn model to all rows in the customer_churn_params table, which would be wasteful and redundant. It would also require implementing logic to perform an upsert into the predictions table, which would be more complex than using the merge statement.
Option B would require converting the batch job to a Structured Streaming job, which would involve changing the data ingestion and processing logic. It would also require using the complete output mode, which would output the entire result table every time there is a change in the source data, which would be inefficient and costly.
Option C would require calculating the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers, which would be computationally expensive and prone to errors. It would also require storing and accessing the previous predictions, which would add extra storage and I/O costs.
Option D would require modifying the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written, which would add extra complexity and overhead to the data engineering job. It would also require using this field to identify records written on a particular date, which would be less accurate and reliable than using the change data feed.
References: Merge, Change data feed

NEW QUESTION # 199
A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue
is affecting all of their sequentially run queries. They ask the data engineering team for help. The data
engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not
used by any other user.
Which of the following approaches can the data engineering team use to improve the latency of the data
analyst's queries?

A. They can increase the maximum bound of the SQL endpoint's scaling range
B. They can turn on the Auto Stop feature for the SQL endpoint
C. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to
"Reliability Optimized"
D. They can increase the cluster size of the SQL endpoint
E. They can turn on the Serverless feature for the SQL endpoint

Answer: D

NEW QUESTION # 200
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
B. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
C. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
D. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
E. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.

Answer: E

Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.

NEW QUESTION # 201
......

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