[2026] Databricks-Certified-Data-Engineer-Professional PDF Questions - Perfect Prospect To Go With DumpsTorrent Practice Exam
Databricks Databricks-Certified-Data-Engineer-Professional Pdf Questions - Outstanding Practice To your Exam
NEW QUESTION # 10
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?
- A. DESCRIBE DETAIL dev.pii test
- B. SHOW TABLES dev
- C. SHOW TBLPROPERTIES dev.pii test
- D. DESCRIBE EXTENDED dev.pii test
- E. DESCRIBE HISTORY dev.pii test
Answer: D
Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.
NEW QUESTION # 11
A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
- A. return spark.readStream.table("bronze")
- B. def new_records():
- C. return spark.readStream.load("bronze")
- D. return spark.read.option("readChangeFeed", "true").table ("bronze")
- E.
Answer: E
Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html
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NEW QUESTION # 12
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?
- A. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
- B. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
- C. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
- D. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
Answer: D
Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.
NEW QUESTION # 13
The data engineering team maintains the following code:
Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?
- A. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
- B. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
- C. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
- D. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
- E. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
Answer: E
Explanation:
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables.
NEW QUESTION # 14
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.
Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?
- A. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
- B. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
- C. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
- D. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
- E. No; the change data feed only tracks inserts and updates not deleted records.
Answer: C
Explanation:
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The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.
NEW QUESTION # 15
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. 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.
- B. 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.
- C. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
- D. 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.
- E. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
Answer: D
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.
NEW QUESTION # 16
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
- A. The five Minute Load Average remains consistent/flat
- B. Total Disk Space remains constant
- C. Bytes Received never exceeds 80 million bytes per second
- D. CPU Utilization is around 75%
- E. Network I/O never spikes
Answer: D
Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.
NEW QUESTION # 17
A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?
- A. Use coalesce() after the aggregation.
- B. Use broadcast join.
- C. Repartition by region before aggregation.
- D. Caching the DataFrame df.
Answer: C
Explanation:
Repartitioning the DataFrame by the grouping key ensures that records with the same region are colocated in the same partitions before the aggregation runs. This significantly reduces the amount of data shuffled during the groupBy operation, leading to more efficient execution.
NEW QUESTION # 18
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
- B. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
- C. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- D. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- E. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
Answer: B
Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.
NEW QUESTION # 19
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:
A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?
- A. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
- B. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
- C. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
- D. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
- E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
Answer: C
Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated.
NEW QUESTION # 20
A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?
- A. Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
- B. Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
- C. Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.
- D. Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.
Answer: B
Explanation:
The Databricks documentation specifies that for Lakeflow Declarative Pipelines, detailed data quality metrics are logged as events of type expectation_result within the event log. Each record of this type contains fields including expectation_name, dataset_name, passed_records, and failed_records. Filtering on event_type = 'expectation_result' and expanding the details field allows retrieving metrics for each expectation from the most recent pipeline update. While flow_progress provides summary statistics and data_quality events aggregate results, only expectation_result events provide granular, per-expectation metrics required for audit and monitoring automation.
NEW QUESTION # 21
A Delta Lake table representing metadata about content from user has the following schema:
Based on the above schema, which column is a good candidate for partitioning the Delta Table?
- A. Post_id
- B. Date
- C. Post_time
- D. User_id
- E. latitude
Answer: B
Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.
NEW QUESTION # 22
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?
- A. Total VMs: 1
400 GB per Executor
160 Cores / Executor - B. Total VMs: 4
100 GB per Executor
40 Cores/Executor - C. Total VMs: 2
200 GB per Executor
80 Cores / Executor - D. Total VMs: 8
50 GB per Executor
20 Cores / Executor
Answer: A
Explanation:
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html
NEW QUESTION # 23
A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?
- A. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
- B. Add the .whl file to Databricks Git Repos and assume automatic installation.
- C. Set up a private PyPI repository and install via pip index URL.
- D. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
Answer: D
Explanation:
For secure, air-gapped Databricks deployments, the recommended practice is to host dependency files such as .whl packages in Unity Catalog Volumes -- a managed storage layer governed by Unity Catalog.
Once stored in a volume, these files can be safely referenced from cluster-scoped init scripts, which automatically execute installation commands (e.g., pip install
/Volumes/catalog/schema/path/PyYAML.whl) during cluster startup.
This ensures consistent environment setup across clusters and compliance with data governance rules.
User directories (A) lack enterprise security controls; private repositories (C) are not viable in air- gapped setups; and Git repos (D) do not trigger package installation. Therefore, B is the correct and officially approved method.
NEW QUESTION # 24
A view is registered with the following code:
Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?
- A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
- B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
- C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
- D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
Answer: C
NEW QUESTION # 25
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