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Databricks Databricks-Certified-Professional-Data-Engineer Dumps

Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

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?

Options:

A.

The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

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.

Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

Question 2

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.

One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.

What approach would allow them to do this?

Options:

A.

Maintain data quality rules in a Delta table outside of this pipeline’s target schema, providing the schema name as a pipeline parameter.

B.

Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

C.

Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

D.

Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

Question 3

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Network I/O never spikes

D.

Total Disk Space remains constant

E.

CPU Utilization is around 75%

Question 4

The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.

Which approach will ensure that this requirement is met?

Options:

A.

Whenever a database is being created, make sure that the location keyword is used

B.

When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

C.

Whenever a table is being created, make sure that the location keyword is used.

D.

When tables are created, make sure that the external keyword is used in the create table statement.

E.

When the workspace is being configured, make sure that external cloud object storage has been mounted.

Question 5

A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

Options:

A.

Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.

B.

The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

C.

Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.

D.

One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.

E.

The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

Question 6

A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.

Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

Options:

A.

Use Repos to merge all differences and make a pull request back to the remote repository.

B.

Use repos to merge all difference and make a pull request back to the remote repository.

C.

Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.

D.

Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

Question 7

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?

Options:

A.

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.

B.

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.

C.

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.

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.

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.

Question 8

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.

What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

Options:

A.

Can manage

B.

Can edit

C.

Can run

D.

Can Read

Question 9

A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.

How can the data engineer fix this?

Options:

A.

Convert the list of configuration values to a dictionary of table settings, using table names as keys.

B.

Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

C.

Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

D.

Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

Question 10

The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.

The following logic is used to process these records.

MERGE INTO customers

USING (

SELECT updates.customer_id as merge_ey, updates .*

FROM updates

UNION ALL

SELECT NULL as merge_key, updates .*

FROM updates JOIN customers

ON updates.customer_id = customers.customer_id

WHERE customers.current = true AND updates.address <> customers.address

) staged_updates

ON customers.customer_id = mergekey

WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN

UPDATE SET current = false, end_date = staged_updates.effective_date

WHEN NOT MATCHED THEN

INSERT (customer_id, address, current, effective_date, end_date)

VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null)

Which statement describes this implementation?

Options:

A.

The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

B.

The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

C.

The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

D.

The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Question 11

A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

Options:

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.

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.

D.

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.

E.

The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Question 12

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.

What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

Options:

A.

Can Manage

B.

Can Edit

C.

No permissions

D.

Can Read

E.

Can Run

Question 13

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.

Which kind of the test does the above line exemplify?

Options:

A.

Integration

B.

Unit

C.

Manual

D.

functional

Question 14

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.

The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

Options:

A.

No; Delta Lake manages streaming checkpoints in the transaction log.

B.

Yes; both of the streams can share a single checkpoint directory.

C.

No; only one stream can write to a Delta Lake table.

D.

Yes; Delta Lake supports infinite concurrent writers.

E.

No; each of the streams needs to have its own checkpoint directory.

Question 15

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.

Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

Options:

A.

Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.

B.

The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

C.

Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

D.

Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

E.

The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

Question 16

The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.

A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have

Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.

Which statement captures best practices for this situation?

Options:

A.

Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

B.

All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.

C.

In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

D.

Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.

Question 17

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?

Options:

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.

Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

C.

Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

D.

Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

E.

Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

Question 18

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. Events are recorded once per minute per device.

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.

Options:

A.

to_interval("event_time", "5 minutes").alias("time")

B.

window("event_time", "5 minutes").alias("time")

C.

"event_time"

D.

window("event_time", "10 minutes").alias("time")

E.

lag("event_time", "10 minutes").alias("time")

Question 19

A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.

When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Total Disk Space remains constant

D.

Network I/O never spikes

E.

Overall cluster CPU utilization is around 25%

Question 20

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

Options:

A.

"Can Manage" privileges on the required cluster

B.

Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

C.

Cluster creation allowed. "Can Attach To" privileges on the required cluster

D.

"Can Restart" privileges on the required cluster

E.

Cluster creation allowed. "Can Restart" privileges on the required cluster

Question 21

Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

Options:

A.

configure

B.

fs

C.

jobs

D.

libraries

E.

workspace

Question 22

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:

Which statement describes the execution and results of running the above query multiple times?

Options:

A.

Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.

B.

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.

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, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.

E.

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.

Question 23

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?

Options:

A.

Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.

B.

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.

C.

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.

D.

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.

E.

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.

Question 24

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.

Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

Options:

A.

Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.

B.

Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

C.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

D.

Both commands will fail. No new variables, tables, or views will be created.

E.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

Question 25

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.

A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.

Which statement explains the cause of this failure?

Options:

A.

Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

B.

The activity details table already exists; CHECK constraints can only be added during initial table creation.

C.

The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

D.

The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

E.

The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

Question 26

In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone.

A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.

Why are the cloned tables no longer working?

Options:

A.

The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

B.

Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.

C.

The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

D.

Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

Question 27

Which statement characterizes the general programming model used by Spark Structured Streaming?

Options:

A.

Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

B.

Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

C.

Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

D.

Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.

E.

Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

Question 28

A Delta Lake table representing metadata about content posts from users has the following schema:

user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE

This table is partitioned by the date column. A query is run with the following filter:

longitude < 20 & longitude > -20

Which statement describes how data will be filtered?

Options:

A.

Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

B.

No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

C.

The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

D.

Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

E.

The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Question 29

A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.

Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

Options:

A.

Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.

B.

Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.

C.

The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.

D.

Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.

E.

Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.

Question 30

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?

Options:

A.

Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9

B.

Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.

C.

Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

D.

Merge all changes back to the main branch in the remote Git repository and clone the repo again

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

Question 31

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:

df = spark.read.format("parquet").load(f"/mnt/source/(date)")

Which code block should be used to create the date Python variable used in the above code block?

Options:

A.

date = spark.conf.get("date")

B.

input_dict = input()

date= input_dict["date"]

C.

import sys

date = sys.argv[1]

D.

date = dbutils.notebooks.getParam("date")

E.

dbutils.widgets.text("date", "null")

date = dbutils.widgets.get("date")

Question 32

A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.

The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

Options:

A.

All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

B.

All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

C.

All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

D.

All records from all columns will be displayed with the values in user_ltv.

Question 33

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?

Options:

A.

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.

B.

Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

C.

Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.

D.

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.

Question 34

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.

Which strategy will yield the best performance without shuffling data?

Options:

A.

Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

B.

Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

C.

Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

D.

Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

E.

Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Question 35

A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.

Which code block attempts to perform an invalid stream-static join?

Options:

A.

userLookup.join(streamingDF, ["userid"], how="inner")

B.

streamingDF.join(userLookup, ["user_id"], how="outer")

C.

streamingDF.join(userLookup, ["user_id”], how="left")

D.

streamingDF.join(userLookup, ["userid"], how="inner")

E.

userLookup.join(streamingDF, ["user_id"], how="right")

Question 36

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?

Options:

A.

All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

B.

Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

C.

Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

D.

Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.