[Jun-2024] Databricks Databricks-Certified-Data-Engineer-Professional Official Cert Guide PDF
Exam Databricks-Certified-Data-Engineer-Professional: Databricks Certified Data Engineer Professional Exam - Real4Prep
NEW QUESTION # 42
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?
- A. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
- B. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
- C. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
- D. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
- E. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
Answer: B
Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that 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.
When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs.
NEW QUESTION # 43
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?
- A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
- B. Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
- C. Whenever a table is being created, make sure that the location keyword is used.
- D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
- E. When tables are created, make sure that the external keyword is used in the create table statement.
Answer: C
Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table 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 by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.
NEW QUESTION # 44
Which statement characterizes the general programming model used by Spark Structured Streaming?
- A. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
- B. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
- C. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
- D. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
- E. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
Answer: C
Explanation:
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html
NEW QUESTION # 45
Which statement regarding stream-static joins and static Delta tables is correct?
- A. Stream-static joins cannot use static Delta tables because of consistency issues.
- B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
- C. The checkpoint directory will be used to track updates to the static Delta table.
- D. The checkpoint directory will be used to track state information for the unique keys present in the join.
- E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
Answer: E
Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.
NEW QUESTION # 46
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
- E. 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.
Answer: E
NEW QUESTION # 47
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?
- A. Query's detail screen and Job's detail screen
- B. Stage's detail screen and Executor's log files
- C. Executor's detail screen and Executor's log files
- D. Driver's and Executor's log files
- E. Stage's detail screen and Query's detail screen
Answer: B
Explanation:
In the Spark UI, the Stage's detail screen provides key metrics about each stage of a job, including the amount of data that has been spilled to disk. If you see a high number in the "Spill (Memory)" or "Spill (Disk)" columns, it's an indication that a partition is spilling to disk.
The Executor's log files can also provide valuable information about spill. If a task is spilling a lot of data, you'll see messages in the logs like "Spilling UnsafeExternalSorter to disk" or "Task memory spill". These messages indicate that the task ran out of memory and had to spill data to disk.
NEW QUESTION # 48
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
- A. Network I/O never spikes
- B. The five Minute Load Average remains consistent/flat
- C. CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
- D. Bytes Received never exceeds 80 million bytes per second
- E. Total Disk Space remains constant
Answer: C
Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.
NEW QUESTION # 49
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?
- A. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
- B. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
- C. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.
- D. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
- E. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
Answer: D
Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
NEW QUESTION # 50
The following code has been migrated to a Databricks notebook from a legacy workload:
The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
- A. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
- B. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
- C. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
- D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
- E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
Answer: E
Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.
NEW QUESTION # 51
A Delta Lake table representing metadata about content from user has the following schema:
Based on the above schema, which column is a good candidate for partitioning the Delta Table?
- A. latitude
- B. Post_id
- C. Post_time
- D. Date
- E. User_id
Answer: D
Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.
NEW QUESTION # 52
A table named user_ltv is being used to create a view that will be used by data analysts 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:
email STRING, age INT, ltv INT
The following view definition is executed:
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?
- 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 values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
- E. All records from all columns will be displayed with the values in user_ltv.
Answer: A
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.
NEW QUESTION # 53
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.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
- A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
- 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. Both commands will fail. No new variables, tables, or views will be created.
- D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
- E. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
Answer: A
Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view.
Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter.
NEW QUESTION # 54
A Delta Lake table was created with the below query:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
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. The table reference in the metastore is updated and all data files are moved.
- B. The table reference in the metastore is updated and no data is changed.
- C. All related files and metadata are dropped and recreated in a single ACID transaction.
- D. A new Delta transaction log Is created for the renamed table.
- E. The table name change is recorded in the Delta transaction log.
Answer: B
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 # 55
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?
- A. Yields faster deployment and execution times
- B. Ensures that all steps interact correctly to achieve the desired end result
- C. Improves the quality of your data
- D. Troubleshooting is easier since all steps are isolated and tested individually
- E. Validates a complete use case of your application
Answer: D
Explanation:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.
NEW QUESTION # 56
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?
- A. spark.sql.autoBroadcastJoinThreshold
- B. spark.sql.adaptive.advisoryPartitionSizeInBytes
- C. spark.sql.adaptive.coalescePartitions.minPartitionNum
- D. spark.sql.files.openCostInBytes
- E. spark.sql.files.maxPartitionBytes
Answer: E
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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.
NEW QUESTION # 57
Which of the following is true of Delta Lake and the Lakehouse?
- A. Z-order can only be applied to numeric values stored in Delta Lake tables
- B. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
- C. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
- D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
- E. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
Answer: E
Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. 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 plan. This can significantly improve the query performance and reduce the I/O cost.
NEW QUESTION # 58
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 Edit
- B. Can Run
- C. Can Manage
- D. Can Read
- E. No permissions
Answer: D
NEW QUESTION # 59
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?
- A. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
- B. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from - C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
- D. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
Answer: C
Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.
NEW QUESTION # 60
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?
- A. Set the configuration delta.deduplicate = true.
- B. Rely on Delta Lake schema enforcement to prevent duplicate records.
- C. VACUUM the Delta table after each batch completes.
- D. Perform an insert-only merge with a matching condition on a unique key.
- E. Perform a full outer join on a unique key and overwrite existing data.
Answer: D
Explanation:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.
NEW QUESTION # 61
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. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
- B. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
- C. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
- D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
Answer: C
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 # 62
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
- A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
- B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
- C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
- D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
Answer: D
Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.
NEW QUESTION # 63
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