Databricks-Certified-Professional-Data-Engineer試験の準備方法｜正確的なDatabricks-Certified-Professional-Data-Engineer資格復習テキスト試験｜便利なDatabricks Certified Professional Data Engineer Exam最新な問題集

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Databricks Certified Professional Data Engineer Examは、Databricksを使用したデータエンジニアリングに関連する広範なトピックをカバーしており、データの取り込み、データ変換、データストレージ、およびデータオーケストレーションを含みます。試験はまた、Delta Lake、Apache Spark、およびDatabricks RuntimeなどのDatabricksツールやテクノロジーの熟達度も試験します。試験に合格することにより、候補者がDatabricksを使用して効率的かつスケーラブルなデータパイプラインを設計、構築、および管理するために必要なスキルと知識を持っていることを証明します。この認定資格は、業界の主要な組織によって認められているため、候補者の信頼性と市場価値を高めます。

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q205-Q210):

質問 # 205
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. await("event_time + '10 minutes'")
B. awaitArrival("event_time", "10 minutes")
C. slidingWindow("event_time", "10 minutes")
D. withWatermark("event_time", "10 minutes")
E. delayWrite("event_time", "10 minutes")

正解：D

解説：
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. Reference:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations: https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html

質問 # 206
A Delta Lake table was created with the below query:
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 no data is changed.
B. A new Delta transaction log Is created for the renamed table.
C. The table reference in the metastore is updated and all data files are moved.
D. The table name change is recorded in the Delta transaction log.
E. All related files and metadata are dropped and recreated in a single ACID transaction.

正解：A

解説：
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. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

質問 # 207
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format( " parquet " )
.load( " /mnt/raw_orders/ " )
.withWatermark( " time " , " 2 hours " )
.dropDuplicates([ " customer_id " , " order_id " ])
.writeStream
.trigger(once=True)
.table( " orders " )
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
B. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
C. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
D. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.

正解：D

解説：
* Exact extract: "dropDuplicates with watermark performs stateful deduplication on the keys within the watermark delay."
* Exact extract: "Records older than the event-time watermark are considered late and may be dropped." References: Structured Streaming watermarking and deduplication; One-time trigger.

質問 # 208
You have accidentally deleted records from a table called transactions, what is the easiest way to restore the records deleted or the previous state of the table? Prior to deleting the version of the table is 3 and after delete the version of the table is 4.

A. RESTORE TABLE transactions FROM VERSION as of 4
B. 1.INSERT INTO OVERWRITE transactions
2.SELECT * FROM transactions VERSION AS OF 4
3.INTERSECT
4.SELECT * FROM transactions
C. RESTORE TABLE transactions TO VERSION as of 3
C .
1.INSERT INTO OVERWRITE transactions
2.SELECT * FROM transactions VERSION AS OF 3
3.MINUS
4.SELECT * FROM transactions
D. COPY OVERWRITE transactions from VERSION as of 3

正解：C

解説：
Explanation
RESTORE (Databricks SQL) | Databricks on AWS
1.RESTORE [TABLE] table_name [TO] time_travel_version
Time travel supports using timestamp or version number
1.time_travel_version
2. { TIMESTAMP AS OF timestamp_expression |
3. VERSION AS OF version }
*timestamp_expression can be any one of:
*'2018-10-18T22:15:12.013Z', that is, a string that can be cast to a timestamp
*cast('2018-10-18 13:36:32 CEST' as timestamp)
*'2018-10-18', that is, a date string
*current_timestamp() - interval 12 hours
*date_sub(current_date(), 1)
*Any other expression that is or can be cast to a timestamp

質問 # 209
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. Total Disk Space remains constant
B. The five Minute Load Average remains consistent/flat
C. Bytes Received never exceeds 80 million bytes per second
D. Network I/O never spikes
E. CPU Utilization is around 75%

正解：E

解説：
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.
References:
* Ganglia Monitoring System: Ganglia Documentation
* Databricks Documentation on Monitoring: Databricks Cluster Monitoring

質問 # 210
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