良質なDatabricks-Certified-Professional-Data-EngineerのPDF問題集でDatabricks-Certified-Professional-Data-Engineer試験問題を試せます [Q10-Q28]

4.3/5 - (7 votes)

良質なDatabricks-Certified-Professional-Data-EngineerのPDF問題集でDatabricks-Certified-Professional-Data-Engineer試験問題を試せます

一番最新のDatabricks Databricks-Certified-Professional-Data-Engineer試験問題集PDF2024年更新

Databricks-Certified-Professional-Data-Engineer試験を受けるには、候補者は最初にDatabricks認定されたアソシエイトデータアナリストおよびDataBricks認定されたアソシエイトデータエンジニア試験を完了する必要があります。これらの試験は、専門レベルの試験に合格するために必要な知識とスキルの基盤を提供します。候補者はまた、Databricksを扱う経験があり、そのさまざまな機能と機能に精通している必要があります。

質問 10
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?

%sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

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.

%sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

%sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

質問 11
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 logical table will persist the query plan to the Hive Metastore in the Databricks control plane.

An external table will be created in the storage container mounted to /mnt/finance eda bucket.

A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

An managed table will be created in the storage container mounted to /mnt/finance eda bucket.

A managed table will be created in the DBFS root storage container.

質問 12
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 the date variable:

Assume that the fields customer_id and order_id serve 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?

Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.

Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

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.

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.

Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

質問 13
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job’s schedule and configuration?

They can link the Job to notebooks that are a part of a Databricks Repo.

They can submit the Job once on a Job cluster.

They can download the JSON equivalent of the job from the Job’s page.

They can submit the Job once on an all-purpose cluster.

They can download the XML description of the Job from the Job’s page

質問 14
When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday’s version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = ‘timestamp’

TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)

SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)

SHOW HISTORY table_name AS OF date_sub(current_date(), 1)

質問 15
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

Use &Pip install in a notebook cell

Run source env/bin/activate in a notebook setup script

Install libraries from PyPi using the cluster UI

Use &sh install in a notebook cell

質問 16
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store –
If this statement is executed by a workspace admin, which result will occur?

Nothing will occur until a COMMIT command is executed.

The table will be removed from the catalog but the data will remain in storage.

The table will be removed from the catalog and the data will be deleted.

An error will occur because Delta Lake prevents the deletion of production data.

Data will be marked as deleted but still recoverable with Time Travel.

質問 17
Why does AUTO LOADER require schema location?

Schema location is used to store user provided schema

Schema location is used to identify the schema of target table

AUTO LOADER does not require schema location, because its supports Schema evolution

Schema location is used to store schema inferred by AUTO LOADER

Schema location is used to identify the schema of target table and source table

質問 18
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.

No: the change data feed only tracks inserts and updates not deleted records.

No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

質問 19
How do you handle failures gracefully when writing code in Pyspark, fill in the blanks to complete the below statement
1._____
2.
3. Spark.read.table(“table_name”).select(“column”).write.mode(“append”).SaveAsTable(“new_table_name”)
4.
5._____
6.
7. print(f”query failed”)

try: failure:

try: catch:

try: except:

try: fail:

try: error:

質問 20
The Delta Live Tables Pipeline is configured to run in Development mode using the Triggered Pipeline Mode.
what is the expected outcome after clicking Start to update the pipeline?

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional development and testing

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional development and testing

All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline

質問 21
You are currently working with the second team and both teams are looking to modify the same notebook, you noticed that the second member is copying the notebooks to the personal folder to edit and replace the collaboration notebook, which notebook feature do you recommend to make the process easier to collaborate.

Databricks notebooks should be copied to a local machine and setup source control lo-cally to version the notebooks

Databricks notebooks support automatic change tracking and versioning

Databricks Notebooks support real-time coauthoring on a single notebook

Databricks notebooks can be exported into dbc archive files and stored in data lake

Databricks notebook can be exported as HTML and imported at a later time

質問 22
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.

A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.

The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.

An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.

質問 23
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?

”Read” permissions should be set on a secret key mapped to those credentials that will be used by a given team.

No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.

“Read” permissions should be set on a secret scope containing only those credentials that will be used by a given team.

“Manage” permission should be set on a secret scope containing only those credentials that will be used by a given team.

質問 24
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

spark.sql.files.maxPartitionBytes

spark.sql.autoBroadcastJoinThreshold

spark.sql.files.openCostInBytes

spark.sql.adaptive.coalescePartitions.minPartitionNum

spark.sql.adaptive.advisoryPartitionSizeInBytes

質問 25
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?

The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

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.

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

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

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

質問 26
What is the purpose of gold layer in Multi hop architecture?

Optimizes ETL throughput and analytic query performance

Eliminate duplicate records

Preserves grain of original data, without any aggregations

Data quality checks and schema enforcement

Optimized query performance for business-critical data

質問 27
A data engineer has set up a notebook to automatically process using a Job. The data engineer’s manager wants
to version control the schedule due to its complexity.
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of
the Job’s schedule?

They can download the JSON description of the Job from the Job’s page

They can submit the Job once on an all-purpose cluster

They can link the Job to notebooks that are a part of a Databricks Repo

They can submit the Job once on a Job cluster

They can download the XML description of the Job from the Job’s page

質問 28
You noticed that colleague is manually copying the notebook with _bkp to store the previous ver-sions, which of the following feature would you recommend instead.

Databricks notebooks support change tracking and versioning

Databricks notebooks should be copied to a local machine and setup source control locally to version the notebooks

Databricks notebooks can be exported into dbc archive files and stored in data lake

Databricks notebook can be exported as HTML and imported at a later time

Databricks認定プロフェッショナルデータエンジニア試験は、データエンジニアリングの広範な知識と経験を必要とする厳格な認定試験です。候補者は、データモデリング、データウェアハウジング、ETL、データガバナンス、データセキュリティなど、データエンジニアリングの概念を深く理解する必要があります。さらに、Apache Spark、Delta Lake、MLFlowなどのDatabricksツールとテクノロジーの操作経験が必要です。この試験に合格すると、候補者は、DataBricksプラットフォーム上のデータパイプラインを構築および最適化するために必要なスキルと知識を持っていることが示されています。

Databricks Certified Professional Data Engineer試験は、複数選択式の問題で構成され、オンラインで実施されます。この試験は、Sparkアーキテクチャ、Sparkプログラミング、データ処理、データ分析、およびデータモデリングなど、様々な分野における候補者の能力を測定することを意図しています。また、Sparkのパフォーマンス最適化やSparkアプリケーションのトラブルシューティング能力も試験されます。この試験を受験する個人には、少なくとも2年間のビッグデータ技術とApache Sparkの実務経験があることが推奨されます。

100%無料Databricks Certification Databricks-Certified-Professional-Data-Engineer問題集PDFお試しサンプル認定ガイドカバー率：https://www.passtest.jp/Databricks/Databricks-Certified-Professional-Data-Engineer-shiken.html