Databricks Certified Data Engineer Professional Exam
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Databricks Certified Data Engineer Professional Exam
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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. 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.
Answer: B Explanation: Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References: Table Access Control: https://docs.databricks.com/security/access-control/tableacls/index.html DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table
Question # 2
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: 8 • 50 GB per Executor • 20 Cores / Executor C. • Total VMs: 4 • 100 GB per Executor • 40 Cores/Executor D. • Total VMs:2 • 200 GB per Executor • 80 Cores / Executor
Answer: B Explanation: This is the correct answer because it is the cluster configuration that will result in maximum performance for a job with at least one wide transformation. A wide transformation is a type of transformation that requires shuffling data across partitions, such as join, groupBy, or orderBy. Shuffling can be expensive and time-consuming, especially if there are too many or too few partitions. Therefore, it is important to choose a cluster configuration that can balance the trade-off between parallelism and network overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor will create 8 partitions, each with enough memory and CPU resources to handle the shuffling efficiently. Having fewer VMs with more memory and cores per executor will create fewer partitions, which will reduce parallelism and increase the size of each shuffle block. Having more VMs with less memory and cores per executor will create more partitions, which will increase parallelism but also increase the network overhead and the number of shuffle files. Verified References: [Databricks Certified Data Engineer Professional], under “Performance Tuning” section; Databricks Documentation, under “Cluster configurations” section.
Question # 3
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. 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.
Answer: E 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. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Delta Lake core features” section.
Question # 4
Which statement describes Delta Lake Auto Compaction?
A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB. B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job. C. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written. D. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete. E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
Answer: E Explanation: This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Auto Compaction for Delta Lake on Databricks” section. "Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven’t been compacted previously." https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size
Question # 5
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?
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.
Answer: C Explanation: The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References: Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).
Question # 6
An external object storage container has been mounted to the location
/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:
After the database was successfully created and permissions configured, a member of the
finance team runs the following code: If all users on the finance team are members of the finance group, which statement
describes how the tx_sales table will be created?
A. A logical table will persist the query plan to the Hive Metastore in the Databricks control
plane. B. An external table will be created in the storage container mounted to /mnt/finance eda bucket. C. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane. D. An managed table will be created in the storage container mounted to /mnt/finance eda bucket. E. A managed table will be created in the DBFS root storage container.
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 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.
Answer: C 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. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “Choose a region”
Question # 8
Where in the Spark UI can one diagnose a performance problem induced by not leveraging
predicate push-down?
A. In the Executor's log file, by gripping for "predicate push-down" B. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column C. In the Storage Detail screen, by noting which RDDs are not stored on disk D. In the Delta Lake transaction log. by noting the column statistics E. In the Query Detail screen, by interpreting the Physical Plan
Answer: E Explanation: This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under “Spark Core” section; Databricks Documentation, under “Predicate pushdown” section; Databricks Documentation, under “Query detail page” section.
Question # 9
Which of the following is true of Delta Lake and the Lakehouse?
A. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times. B. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters. C. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times. D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table. E. Z-order can only be applied to numeric values stored in Delta Lake tables
Answer: B Explanation: https://docs.delta.io/2.0.0/table-properties.html Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost. The other options are false because: Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2. Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3. However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands. Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency. Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Zorder can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values. References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]
Question # 10
Which is a key benefit of an end-to-end test?
A. It closely simulates real world usage of your application. B. It pinpoint errors in the building blocks of your application. C. It provides testing coverage for all code paths and branches. D. It makes it easier to automate your test suite
Answer: A Explanation: End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly. References: Software Testing: End-to-End Testing
Question # 11
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. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text. B. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console. C. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default. D. Iterating through a stored secret and printing each character will display secret contents in plain text. E. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
Answer: E 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. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “List secrets” section.
Question # 12
A Databricks SQL dashboard has been configured to monitor the total number of records
present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is
updated?
A. The total count of rows is calculated by scanning all data files B. The total count of rows will be returned from cached results unless REFRESH is run C. The total count of records is calculated from the Delta transaction logs D. The total count of records is calculated from the parquet file metadata E. The total count of records is calculated from the Hive metastore
Which distribution does Databricks support for installing custom Python code packages?
A. sbt B. CRAN C. CRAM D. nom E. Wheels F. jars
Answer: D
Question # 14
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 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 time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place. C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning. D. 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 longterm versioning needs, making a Type 2 approach more viable for performance and scalability.References: Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake
Question # 15
Which statement describes Delta Lake optimized writes?
A. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions. B. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written. C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB. D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
Answer: A Explanation: Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files. References: Databricks documentation on Delta Lake performance tuning: https://docs.databricks.com/delta/optimizations/auto-optimize.html
Question # 16
Which configuration parameter directly affects the size of a spark-partition upon ingestion
of data into Spark?
A. spark.sql.files.maxPartitionBytes B. spark.sql.autoBroadcastJoinThreshold C. spark.sql.files.openCostInBytes D. spark.sql.adaptive.coalescePartitions.minPartitionNum E. spark.sql.adaptive.advisoryPartitionSizeInBytes
Answer: A Explanation: This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified References: [Databricks Certified Data Engineer Professional], under “Spark Configuration” section; Databricks Documentation, under “Available Properties - spark.sql.files.maxPartitionBytes” section.
Question # 17
Two of the most common data locations on Databricks are the DBFS root storage and
external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
A. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems. B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators. C. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions. D. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook E. The DBFS root stores files in ephemeral block volumes attached to the driver, while
mounted directories will always persist saved data to external storage between sessions.
Answer: A Explanation: DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE
Question # 18
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.
Which statement describes this implementation?
A. The customers table is implemented as a Type 3 table; old values are maintained as a
new column alongside the current value. B. 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. C. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values. D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained. E. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
Answer: A Explanation: The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Merge Into (Delta Lake on Databricks)” section.
Question # 19
The data engineering team has configured a job to process customer requests to be
forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta
Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at
1am each Sunday. The total duration of this job is less than one hour. Every Monday at
3am, a batch job executes a series of VACUUM commands on all Delta Lake tables
throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality.
They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses
this concern?
A. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours. B. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day. C. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges. D. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes. E. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
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?
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")
Answer: E Explanation: In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins. References: Structured Streaming Programming Guide: Join Operations Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins
Question # 21
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below
filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?
A. All records are cached to an operational database and then the filter is applied B. The Parquet file footers are scanned for min and max statistics for the latitude column C. All records are cached to attached storage and then the filter is applied D. The Delta log is scanned for min and max statistics for the latitude column E. The Hive metastore is scanned for min and max statistics for the latitude column
Answer: D Explanation: This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; [Databricks Documentation], under “Optimizations - Data Skipping” section. In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file: - Total number of records - Minimum value in each column of the first 32 columns of the table - Maximum value in each column of the first 32 columns of the table - Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter. For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column
Question # 22
A user wants to use DLT expectations to validate that a derived table report contains all
records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table
definition. Which approach would allow using DLT expectations to validate all expected records are
present in this table?
A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table. B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
Answer: D Explanation: To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table. References: Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations
Question # 23
Which statement describes integration testing?
A. Validates interactions between subsystems of your application B. Requires an automated testing framework C. Requires manual intervention D. Validates an application use case E. Validates behavior of individual elements of your application
Answer: D Explanation: This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services. Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under “Testing” section; Databricks Documentation, under “Integration testing” section.
Question # 24
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?
A. Can Manage B. Can Edit C. No permissions D. Can Read E. Can Run Answer:
C Explanation: This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the Question No : 89 Question No : 90 Databricks Databricks-Certified-Professional-Data-Engineer : Practice Test 7 production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “Notebook permissions” section.
Question # 25
Which Python variable contains a list of directories to be searched when trying to locate
required modules?
A. importlib.resource path B. ,sys.path C. os-path D. pypi.path E. pylib.source
Answer: B
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