Databricks-Certified-Professional-Data-Engineer無料問題集「Databricks Certified Professional Data Engineer」

質問 1

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

（A）

（B）

（C）return spark.read.option("readChangeFeed", "true").table ("bronze")

（D）return spark.readStream.load("bronze")

（E）return spark.readStream.table("bronze")

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

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 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.

（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 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.

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

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 name change is recorded in the Delta transaction log.

（B）The table reference in the metastore is updated and no data is changed.

（C）The table reference in the metastore is updated and all data files are moved.

（D）A new Delta transaction log Is created for the renamed table.

（E）All related files and metadata are dropped and recreated in a single ACID transaction.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

Which statement describes integration testing?

（A）Requires manual intervention

（B）Validates behavior of individual elements of your application

（C）Validates interactions between subsystems of your application

（D）Validates an application use case

（E）Requires an automated testing framework

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

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）Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

（C）Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

（D）Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.

（E）Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

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）Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

（B）All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.

（C）Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

（D）Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

（E）Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

The data science team has requested assistance in accelerating queries on free form text from user reviews.
The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

（A）Text data cannot be stored with Delta Lake.

（B）Delta Lake statistics are only collected on the first 4 columns in a table.

（C）The Delta log creates a term matrix for free text fields to support selective filtering.

（D）ZORDER ON review will need to be run to see performance gains.

（E）Delta Lake statistics are not optimized for free text fields with high cardinality.

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

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

（A）Run source env/bin/activate in a notebook setup script

（B）Install libraries from PyPi using the cluster UI

（C）Use &Pip install in a notebook cell

（D）Use &sh install in a notebook cell

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

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 overall size of data in the table

（B）Databricks has autotuned to a smaller target file size based on the amount of data in each partition

（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 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

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 cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

（B）Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

（C）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.

（D）Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Databricks-Certified-Professional-Data-Engineer 無料問題集「Databricks Certified Professional Data Engineer」

弊社を連絡する

関連リンク

トップ試験