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Databricks-Certified-Professional-Data-Engineer試験問題でリアルに更新された問題PDF [Q13-Q38]

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Databricks-Certified-Professional-Data-Engineer試験問題でリアルに更新された問題PDF

合格させる無料保証付きクイズ2023年最新の実際に出ると確認されたDatabricks

質問 # 13
Which of the following commands can be used to query a delta table?

  • A. 1.%python
    2.execute.sql("select * from table")
  • B. Both A & B
    (Correct)
  • C. 1.%sql
    2.Select * from table_name
  • D. 1.%python
    2.spark.sql("select * from table_name")
  • E. 1.%python
    2.delta.sql("select * from table")

正解：B

解説：
Explanation
The answer is both options A and B
Options C and D are incorrect because there is no command in Spark called execute.sql or delta.sql


質問 # 14
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

  • A. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: Unlimited
  • B. Cluster: New Job Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1
  • C. Cluster: Existing All-Purpose Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1
  • D. Cluster: Existing All-Purpose Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1
  • E. Cluster: Existing All-Purpose Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1

正解：B

解説：
Explanation
This is the best configuration for scheduling Structured Streaming jobs for production, as it automatically recovers from query failures and keeps costs low. A new job cluster is created for each run of the job and terminated when the job completes, which saves costs and avoids resource contention. Retries are not needed for Structured Streaming jobs, as they can automatically recover from failures using checkpointing and write-ahead logs. Maximum concurrent runs should be set to 1 to avoid duplicate output or data loss. Verified References: Databricks Certified Data Engineer Professional, under "Monitoring & Logging" section; Databricks Documentation, under "Schedule streaming jobs" section.


質問 # 15
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. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
  • C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
  • D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

正解：D

解説：
Explanation
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. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.


質問 # 16
You are tasked to set up a set notebook as a job for six departments and each department can run the task parallelly, the notebook takes an input parameter dept number to process the data by department, how do you go about to setup this up in job?

  • A. A task in the job cannot take an input parameter, create six notebooks with hardcoded dept number and setup six tasks with linear dependency in the job
  • B. Use a single notebook as task in the job and use dbutils.notebook.run to run each note-book with parameter in a different cell
  • C. A task accepts key-value pair parameters, creates six tasks pass department number as parameter foreach task with no dependency in the job as they can all run in parallel.
    (Correct)
  • D. A parameter can only be passed at the job level, create six jobs pass department number to each job with no job dependency
  • E. A parameter can only be passed at the job level, create six jobs pass department number to each job with linear job dependency

正解：C

解説：
Explanation
Here is how you setup
Create a single job and six tasks with the same notebook and assign a different parameter for each task , Graphical user interface, text, application, email Description automatically generated

All tasks are added in a single job and can run parallel either using single shared cluster or with individual clusters.
Graphical user interface, application, Teams Description automatically generated


質問 # 17
Newly joined data analyst requested read-only access to tables, assuming you are owner/admin which section of Databricks platform is going to facilitate granting select access to the user

  • A. Admin console
  • B. Data explorer
  • C. User settings
  • D. Azure RBAC
  • E. Azure Databricks control pane IAM

正解：B

解説：
Explanation
Anser is Data Explorer
https://docs.databricks.com/sql/user/data/index.html
Data explorer lets you easily explore and manage permissions on databases and tables. Users can view schema details, preview sample data, and see table details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions.
To open data explorer, click Data in the sidebar.


質問 # 18
You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

  • A. SQL Endpoints
  • B. STRUCTURED STREAMING with MULTI HOP
  • C. JOBS and TASKS
  • D. DELTA LIVE TABLES
  • E. AUTO LOADER

正解：D

解説：
Explanation
The answer is, DELTA LIVE TABLES
DLT simplifies data dependencies by building DAG-based joins between live tables. Here is a view of how the dag looks with data dependencies without additional meta data,
1.create or replace live view customers
2.select * from customers;
3.
4.create or replace live view sales_orders_raw
5.select * from sales_orders;
6.
7.create or replace live view sales_orders_cleaned
8.as
9.select sales.* from
10.live.sales_orders_raw s
11. join live.customers c
12.on c.customer_id = s.customer_id
13.where c.city = 'LA';
14.
15.create or replace live table sales_orders_in_la
16.selects from sales_orders_cleaned;
Above code creates below dag

Documentation on DELTA LIVE TABLES,
https://databricks.com/product/delta-live-tables
https://databricks.com/blog/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.htm DELTA LIVE TABLES, addresses below challenges when building ETL processes
1.Complexities of large scale ETL
a.Hard to build and maintain dependencies
b.Difficult to switch between batch and stream
2.Data quality and governance
a.Difficult to monitor and enforce data quality
b.Impossible to trace data lineage
3.Difficult pipeline operations
a.Poor observability at granular data level
b.Error handling and recovery is laborious


質問 # 19
Which statement describes Delta Lake Auto Compaction?

  • A. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
  • B. 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.
  • C. 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.
  • D. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
  • E. 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.

正解：C

解説：
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.


質問 # 20
You were asked to write python code to stop all running streams, which of the following command can be used to get a list of all active streams currently running so we can stop them, fill in the blank.
1.for s in _______________:
2. s.stop()

  • A. spark.streams.getActive
  • B. spark.streams.active
  • C. Spark.getActiveStreams()
  • D. getActiveStreams()
  • E. activeStreams()

正解：B


質問 # 21
You are still noticing slowness in query after performing optimize which helped you to resolve the small files problem, the column(transactionId) you are using to filter the data has high cardinality and auto incrementing number. Which delta optimization can you enable to filter data effectively based on this column?

  • A. Perform Optimize with Zorder on transactionId
    (Correct)
  • B. Create BLOOM FLTER index on the transactionId
  • C. Increase the driver size and enable delta optimization
  • D. transactionId has high cardinality, you cannot enable any optimization.
  • E. Increase the cluster size and enable delta optimization

正解：A

解説：
Explanation
The answer is, perform Optimize with Z-order by transactionid
Here is a simple explanation of how Z-order works, once the data is naturally ordered, when a flle is scanned it only brings the data it needs into spark's memory Based on the column min and max it knows which data files needs to be scanned.
Table Description automatically generated

Graphical user interface, diagram, application Description automatically generated


質問 # 22
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?

  • A. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL
  • B. They can schedule the query to run every 12 hours from the Jobs UI
  • C. They can schedule the query to run every 1 day from the Jobs UI
  • D. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL
  • E. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL

正解：A


質問 # 23
Which of the following statement is true about Databricks repos?

  • A. A workspace can only have one instance of git integration
  • B. Databricks repos allow you to comment and commit code changes and push them to a remote branch
  • C. You cannot create a new branch in Databricks repos
  • D. Databricks Repos and Notebook versioning are the same features
  • E. You can approve the pull request if you are the owner of Databricks repos

正解：B

解説：
Explanation
See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops Diagram Description automatically generated


質問 # 24
Below table temp_data has one column called raw contains JSON data that records temperature for every four hours in the day for the city of Chicago, you are asked to calculate the maximum temperature that was ever recorded for 12:00 PM hour across all the days. Parse the JSON data and use the necessary array function to calculate the max temp.
Table: temp_date
Column: raw
Datatype: string

Expected output: 58

  • A. 1.select max(raw.chicago.temp[3]) from temp_data
  • B. 1.select max(from_json(raw:chicago[3].temp[3],'array<int>')) from temp_data
  • C. 1.select array_max(raw.chicago[*].temp[3]) from temp_data
  • D. 1.select array_max(from_json(raw:chicago[*].temp[3],'array<int>')) from temp_data
  • E. 1.select array_max(from_json(raw['chicago'].temp[3],'array<int>')) from temp_data

正解：D

解説：
Explanation
Note: This is a difficult question, more likely you may see easier questions similar to this but the more you are prepared for the exam easier it is to pass the exam.
Use this below link to look for more examples, this will definitely help you,
https://docs.databricks.com/optimizations/semi-structured.html
Here is the solution, step by step
Text Description automatically generated

Use this below link to look for more examples, this will definitely help you,
https://docs.databricks.com/optimizations/semi-structured.html
If you want to try this solution use below DDL,
1.create or replace table temp_data
2. as select ' {
3. "chicago":[
4.{"date":"01-01-2021",
5."temp":[25,28,45,56,39,25]
6.},
7.{"date":"01-02-2021",
8."temp":[25,28,49,54,38,25]
9.},
10.{"date":"01-03-2021",
11."temp":[25,28,49,58,38,25]
12. }]
13. }
14. ' as raw
15.
16.select array_max(from_json(raw:chicago[*].temp[3],'array<int>')) from temp_data
17.


質問 # 25
What is the purpose of the silver layer in a Multi hop architecture?

  • A. Optimized query performance for business-critical data
  • B. Eliminates duplicate data, quarantines bad data
  • C. Efficient storage and querying of full, unprocessed history of data
  • D. Replaces a traditional data lake
  • E. Refined views with aggregated data

正解：B

解説：
Explanation
Medallion Architecture - Databricks
Silver Layer:
1. Reduces data storage complexity, latency, and redundency
2. Optimizes ETL throughput and analytic query performance
3. Preserves grain of original data (without aggregation)
4. Eliminates duplicate records
5. production schema enforced
6. Data quality checks, quarantine corrupt data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence


質問 # 26
Which distribution does Databricks support for installing custom Python code packages?

  • A. nom
  • B. CRAN
  • C. Wheels
  • D. CRAM
  • E. jars
  • F. sbt

正解：A


質問 # 27
A junior data engineer has ingested a JSON file into a table raw_table with the following schema:
1. cart_id STRING,
2. items ARRAY<item_id:STRING>
The junior data engineer would like to unnest the items column in raw_table to result in a new table with the
following schema:
1.cart_id STRING,
2.item_id STRING
Which of the following commands should the junior data engineer run to complete this task?

  • A. 1. SELECT cart_id, flatten(items) AS item_id
    2. FROM raw_table;
  • B. 1. SELECT cart_id, slice(items) AS item_id
    2. FROM raw_table;
  • C. 1. SELECT cart_id, reduce(items) AS item_id
    2. FROM raw_table;
  • D. 1. SELECT cart_id, explode(items) AS item_id
    2. FROM raw_table;
  • E. 1. SELECT cart_id, filter(items) AS item_id
    2. FROM raw_table;

正解：D


質問 # 28
You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

  • A. Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process
  • B. AS (CAST(orderTime as DATE))
  • C. GENERATED DEFAULT AS (CAST(orderTime as DATE))
  • D. GENERATED ALWAYS AS (CAST(orderTime as DATE))
    Correct)
  • E. AS DEFAULT (CAST(orderTime as DATE))

正解：D

解説：
Explanation
The answer is, GENERATED ALWAYS AS (CAST(orderTime as DATE))
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-batch#--use-generated-columns Delta Lake supports generated columns which are a special type of columns whose values are au-tomatically generated based on a user-specified function over other columns in the Delta table. When you write to a table with generated columns and you do not explicitly provide values for them, Delta Lake automatically computes the values.
Note: Databricks also supports partitioning using generated column


質問 # 29
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

  • A. SHOW TABLES dev
  • B. DESCRIBE HISTORY dev.pii test
  • C. DESCRIBE DETAIL dev.pii test
  • D. DESCRIBE EXTENDED dev.pii test
  • E. SHOW TBLPROPERTIES dev.pii test

正解：D

解説：
Explanation
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.


質問 # 30
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

  • A. Use the Databricks cluster pools feature to reduce the startup time
  • B. Pin the cluster in the cluster UI page so it is always available to the jobs
  • C. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
  • D. Disable auto termination so the cluster is always running
  • E. Use Databricks Premium edition instead of Databricks standard edition

正解：A

解説：
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated


質問 # 31
While investigating a performance issue, you realized that you have too many small files for a given table, which command are you going to run to fix this issue

  • A. MERGE table_name
  • B. SHRINK table_name
  • C. VACUUM table_name
  • D. COMPACT table_name
  • E. OPTIMIZE table_name

正解：E

解説：
Explanation
The answer is OPTIMIZE table_name,
Optimize compacts small parquet files into a bigger file, by default the size of the files are determined based on the table size at the time of OPTIMIZE, the file size can also be set manually or adjusted based on the workload.
https://docs.databricks.com/delta/optimizations/file-mgmt.html
Tune file size based on Table size
To minimize the need for manual tuning, Databricks automatically tunes the file size of Delta tables based on the size of the table. Databricks will use smaller file sizes for smaller tables and larger file sizes for larger tables so that the number of files in the table does not grow too large.
Table Description automatically generated

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Top of Form


質問 # 32
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

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

正解：A

解説：
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.


質問 # 33
The viewupdatesrepresents an incremental batch of all newly ingested data to be inserted or updated in the customerstable.
The following logic is used to process these records.

Which statement describes this implementation?

  • A. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
  • B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
  • C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
  • D. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
  • E. 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.

正解：E

解説：
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.


質問 # 34
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

  • A. Change the spot instance policy from reliability optimized to cost optimized
  • B. Reduce the size of the SQL Cluster size
  • C. Always use X-small cluster
  • D. Setup the dashboard refresh schedule to end in two weeks
  • E. Reduce the max size of auto scaling from 10 to 5

正解：D

解説：
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks


質問 # 35
How do you access or use tables in the unity catalog?

  • A. catalog_name.table_name
  • B. catalog_name.schema_name.table_name
  • C. schema_name.catalog_name.table_name
  • D. schema_name.table_name
  • E. catalog_name.database_name.schema_name.table_name

正解：B

解説：
Explanation
The answer is catalog_name.schema_name.table_name
Graphical user interface, diagram Description automatically generated

Note: Database and Schema are analogous they are interchangeably used in the Unity catalog.
FYI, A catalog is registered under a metastore, by default every workspace has a default metastore called hive_metastore, with a unity catalog you have the ability to create meatstores and share that across multiple workspaces.

Diagram Description automatically generated


質問 # 36
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

  • A. Each write to the orders table willrun deduplication over the union of new and existing records, ensuringno duplicate records are present.
  • B. Each write to the orders table willonly contain unique records, but newly written records may have duplicates already present in the target table.
  • C. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
  • D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
  • E. Each write to the orders table willonly contain unique records, and only those records without duplicates in the target table will be written.

正解：B

解説：
Explanation
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation usingmergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.


質問 # 37
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order
for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this
process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta
Live Tables?

  • A. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
  • B. They need to create a Delta Live Tables pipeline from the Data page
  • C. They need to refactor their notebook to use Python and the dlt library
  • D. They need to create a Delta Live tables pipeline from the Compute page
  • E. They need to create a Delta Live Tables pipeline from the Jobs page

正解：E


質問 # 38
......


DataBricks認定プロフェッショナルデータエンジニア認定は、DataBricksプラットフォームの使用に関する専門知識を実証したいデータエンジニアにとって貴重な資格です。雇用主に候補者と従業員のスキルを特定して検証する方法を提供します。また、データエンジンがDataBricksプラットフォームを使用してスケーラブルで信頼できるデータパイプラインを構築および維持する習熟度を実証することにより、キャリアを進めるのに役立ちます。

 

トップクラスのDatabricks-Certified-Professional-Data-Engineer練習試験問題：https://www.passtest.jp/Databricks/Databricks-Certified-Professional-Data-Engineer-shiken.html

無料Databricks Certification Databricks-Certified-Professional-Data-Engineer究極の学習ガイド：https://drive.google.com/open?id=1iIgrHzkPv4IFYKDxZ9rl82lTFFEBPeLv

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