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NEW QUESTION # 121
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?
- A. The total number of jobs that have been run in the workspace.
- B. The job_id is returned in this field.
- C. The job_id and number of times the job has been are concatenated and returned.
- D. The number of times the job definition has been run in the workspace.
- E. The globally unique ID of the newly triggered run.
Answer: E
Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.
NEW QUESTION # 122
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. The user_id field 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 named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_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 described account_current table as part of each hourly batch job?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
- E. 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.
Answer: A
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.
NEW QUESTION # 123
A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)
- A. Periodically run OPTIMIZE table_name.
- B. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
- C. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
- D. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
- E. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
Answer: A,D
Explanation:
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.
NEW QUESTION # 124
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?
- A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
- B. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
- C. Merge all changes back to the main branch in the remote Git repository and clone the repo again
- D. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
- E. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
Answer: A
Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.
NEW QUESTION # 125
Which statement describes integration testing?
- A. Validates behavior of individual elements of your application
- B. Requires an automated testing framework
- C. Validates an application use case
- D. Validates interactions between subsystems of your application
- E. Requires manual intervention
Answer: D
Explanation:
Integration testing is a type of software testing where components of the software are gradually integrated and then tested as a unified group.
NEW QUESTION # 126
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?
- A. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
- B. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
- C. Iterating through a stored secret and printing each character will display secret contents in plain text.
- D. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
- E. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the value in plain text.
Answer: B
Explanation:
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets.
NEW QUESTION # 127
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?
- A. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
- B. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
- C. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
- D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
- E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
Answer: C
Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.
NEW QUESTION # 128
A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?
- A. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings") - B. @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings") - C. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings") - D. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
Answer: B
Explanation:
Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect,
@dlt.expect_or_drop, and @dlt.expect_all.
@dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows. Instead, failing rows can be written to a quarantine table.
@dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.
In this case, the requirement is:
Good rows (reading < 120) go to the silver table.
Bad rows (reading >= 120) go to a quarantine table.
Bad rows should not be included in silver.
The correct implementation is Option A, where:
The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.
The quarantine table applies an expectation for reading >= 120, ensuring bad records are captured separately.
Other options are incorrect:
Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.
Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.
Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.
NEW QUESTION # 129
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?
- A. Delete the failed run, disable the cluster policy, and re-execute all tasks.
- B. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
- C. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
- D. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
Answer: B
Explanation:
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.
NEW QUESTION # 130
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?
- A. Configure a job that executes every time new data lands in a given directory.
- B. Schedule a job to execute the pipeline once hour on a new job cluster.
- C. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
- D. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
Answer: B
Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
NEW QUESTION # 131
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?
- A. Set the pipelines, reset, allowed property to false on raw_iot
- B. Set the skipChangeCommits flag to true on bpm_stats
- C. Set the pipelines, reset, allowed property to false on bpm_stats
- D. Set the SkipChangeCommits flag to true raw_lot
Answer: A
Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
NEW QUESTION # 132
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. awaitArrival("event_time", "10 minutes")
- B. await("event_time + `10 minutes'")
- C. slidingWindow("event_time", "10 minutes")
- D. withWatermark("event_time", "10 minutes")
- E. delayWrite("event_time", "10 minutes")
Answer: D
Explanation:
This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming.
NEW QUESTION # 133
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?
- A. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
- B. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
- C. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
- D. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
Answer: A
Explanation:
The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.
NEW QUESTION # 134
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?
- A. Deep clone the table for the analytics team.
- B. Give the analytics team direct access to the production table.
- C. Create a new table for the analytics team using a CTAS statement.
- D. Shallow clone the table for the analytics team.
Answer: D
Explanation:
A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.
NEW QUESTION # 135
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. The Delta log is scanned for min and max statistics for the latitude column
- 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. All records are cached to attached storage and then the filter is applied
Answer: A
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 Reference: [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 # 136
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