Databricks Databricks-Certified-Data-Engineer-Professional絶対合格、Databricks-Certified-Data-Engineer-Professional合格体験談

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling
Databricks Lakehouse Platform	24%	- Delta Lake - Data Management - Unity Catalog - Lakehouse Architecture
Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring
Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality
Data Processing	28%	- Structured Streaming - Spark SQL - Data Transformation - ETL Pipelines

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q24-Q29):

質問 # 24
A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A. df.withColumn ("hashed_email", sha2 ("user_email"))
B. df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
C. df.withColumn ("user_email", F.sha2 ("user_email"))
D. df.withColumn ("user_emai", F.expr("uuid()"))

正解：C

解説：
Hashing the email column replaces the original PII with a deterministic, irreversible value while preserving its role as a unique identifier. This ensures no actual email addresses are copied to the sandbox environment, while still allowing consistent joins or user-level analysis if needed.

質問 # 25
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:
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Proposed query:

Proposed query:
.start("/item_agg")
Which step must also be completed to put the proposed query into production?

A. Specify a new checkpointlocation
B. Increase the shuffle partitions to account for additional aggregates
C. Run REFRESH TABLE delta, /item_agg'
D. Remove .option (mergeSchema', true') from the streaming write
E. Register the data in the "/item_agg" directory to the Hive metastore

正解：A

解説：
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.

質問 # 26
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?

A. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
B. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
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 will only contain unique records, and only those records without duplicates in the target table will be written.

正解：A

解説：
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 using mergeInto.

質問 # 27
A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
B. The source data is valid JSON but does not conform to the defined schema in some way.
C. At some point, the upstream data provider switched everything to multi-line JSON.
D. The badRecordsPath location is accumulating many small files.

正解：B

解説：
Auto Loader quarantines records that cannot be parsed according to the specified schema, even if the JSON itself is well formed. If incoming records contain additional fields, missing fields, or incompatible data types compared to the declared schema, they are treated as invalid and routed to the bad records path.

質問 # 28
A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A. Enable deletion vectors on the Delta table
B. Enable automatic file compaction on writes
C. Partition the Delta table by account_id
D. Enable change data feed on the Delta table

正解：A

解説：
Deletion vectors allow Delta Lake to track row-level deletes and updates without rewriting entire Parquet files. By recording changes separately from the base files, this feature significantly reduces file churn and improves write performance for workloads with frequent row-level modifications such as account-level updates.

質問 # 29
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