Ultimate Guide to Prepare Free Databricks Databricks-Certified-Data-Engineer-Professional Exam Questions & Answer [Q76-Q96]

Ultimate Guide to Prepare Free Databricks Databricks-Certified-Data-Engineer-Professional Exam Questions and Answer

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NEW QUESTION # 76
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

• A. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
• B. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
• C. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
• D. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
• E. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

Answer: A

Explanation:
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.

NEW QUESTION # 77
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

• A. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
• B. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
• C. Instruct the marketing team to download results as a CSV and email them to the sales organization.
• D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
• E. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

Answer: E

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.

NEW QUESTION # 78
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 include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
• D. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
• 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: D

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 Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from constraint, and the target table will not be updated.

NEW QUESTION # 79
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?

• A. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
• B. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
• C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
• D. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
• E. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

Answer: A

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

NEW QUESTION # 80
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

• A. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
• B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
• C. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
• D. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
• E. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

Answer: D

NEW QUESTION # 81
Which statement regarding spark configuration on the Databricks platform is true?

• A. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
• B. Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
• C. When the same spar configuration property is set for an interactive to the same interactive cluster.
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• D. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
• E. Spark configuration properties can only be set for an interactive cluster by creating a global init script.

Answer: A

Explanation:
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.

NEW QUESTION # 82
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

• A. Run source env/bin/activate in a notebook setup script
• B. Install libraries from PyPi using the cluster UI
• C. Use &Pip install in a notebook cell
• D. Use &sh install in a notebook cell

Answer: B

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.

NEW QUESTION # 83
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
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The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

• A.
• B. preds.write.format("delta").save("/preds/churn_preds")
• C.
• D.
• E. preds.write.mode("append").saveAsTable("churn_preds")

Answer: E

NEW QUESTION # 84
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
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Which statement describes the execution and results of running the above query multiple times?

• A. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
• B. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
• C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
• D. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
• E. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Answer: E

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 85
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

• A. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
• B. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
• C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
• D. Because User A created the jobs, their identity will be associated with both the job creation Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from events and the job run events.
• E. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: B

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 86
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_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 skipChangeCommits flag to true on bpm_stats
• B. Set the pipelines, reset, allowed property to false on bpm_stats
• C. Set the pipelines, reset, allowed property to false on raw_iot
• D. Set the SkipChangeCommits flag to true raw_lot

Answer: C

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 # 87
A Delta Lake table in the Lakehouse named customer_parsams is 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.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
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• A. Parse the Delta Lake transaction log to identify all newly written data files.
• B. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
• C. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
• D. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.

Answer: D

Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

NEW QUESTION # 88
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

• A. New fields not be computed for historic records.
• B. Updating the table schema will invalidate the Delta transaction log metadata.
• C. Spark cannot capture the topic partition fields from the kafka source.
• D. Updating the table schema requires a default value provided for each file added.
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• E. New fields cannot be added to a production Delta table.

Answer: A

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.

NEW QUESTION # 89
A Delta Lake table was created with the below query:
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Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

• A. A new Delta transaction log Is created for the renamed table.
• B. The table reference in the metastore is updated and all data files are moved.
• C. All related files and metadata are dropped and recreated in a single ACID transaction.
• D. The table reference in the metastore is updated and no data is changed.
• E. The table name change is recorded in the Delta transaction log.

Answer: D

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.

NEW QUESTION # 90
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. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
• B. "Read" permissions 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 key mapped to those credentials that will be used by a given team.

Answer: B

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 # 91
A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

• A. 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.
• B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
• 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. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
• E. 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.

Answer: A

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 92
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, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
• 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. 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.
• 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:
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 Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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 # 93
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

• A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
• B. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
• C. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
• D. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
• E. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Answer: E

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.

NEW QUESTION # 94
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. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
• B. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
• C. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
• D. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
• E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Answer: B

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.

NEW QUESTION # 95
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. DESCRIBE EXTENDED dev.pii test
• C. SHOW TBLPROPERTIES dev.pii test
• D. DESCRIBE HISTORY dev.pii test
• E. SHOW TABLES dev

Answer: B

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 # 96
......

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