最新的Databricks Certification認證 Associate-Developer-Apache-Spark-3.5考試題庫

問題1

26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A. df_user_non_pii = df_user.drop("name", "email", "birthdate")

B. df_user_non_pii = df_user.remove("name", "email", "birthdate")

C. df_user_non_pii = df_user.select("name", "email", "birthdate")

D. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")

正確答案: A

說明：（僅 VCESoft 成員可見）

問題2

25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A. employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")

B. employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")

C. employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")

D. employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)

正確答案: C

說明：（僅 VCESoft 成員可見）

問題3

A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A. query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()

B. query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()

C. query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()

D. query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()

正確答案: A

說明：（僅 VCESoft 成員可見）

問題4

19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?

A. def shake_256(raw: [pd.Series]) -> pd.Series:

B. def shake_256(raw: [str]) -> [str]:

C. def shake_256(raw: str) -> str:

D. def shake_256(raw: pd.Series) -> pd.Series:

正確答案: D

說明：（僅 VCESoft 成員可見）

問題5

Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A. ALL, DEBUG, FAIL, INFO

B. ERROR, WARN, TRACE, OFF

C. FATAL, NONE, INFO, DEBUG

D. WARN, NONE, ERROR, FATAL

正確答案: B

說明：（僅 VCESoft 成員可見）

問題6

4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A. Increase the value of spark.sql.shuffle.partitions

B. Reduce the value of spark.sql.shuffle.partitions

C. Increase the size of the dataset to create more partitions

D. Enable dynamic resource allocation to scale resources as needed

正確答案: A

說明：（僅 VCESoft 成員可見）

問題7

A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A. df.select("Name").orderBy(df["Name"].asc())

B. df.select("Name").distinct()

C. df.select("Name").distinct().orderBy(df["Name"])

D. df.select("Name").distinct().orderBy(df["Name"].desc())

正確答案: C

說明：（僅 VCESoft 成員可見）

問題8

A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A. Execute their pyspark shell with the option --remote "https://localhost"

B. Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell

C. Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code

D. Execute their pyspark shell with the option --remote "sc://localhost"

E. Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code

正確答案: B,D

說明：（僅 VCESoft 成員可見）

問題9

Which UDF implementation calculates the length of strings in a Spark DataFrame?

A. df.withColumn("length", spark.udf("len", StringType()))

B. spark.udf.register("stringLength", lambda s: len(s))

C. df.select(length(col("stringColumn")).alias("length"))

D. df.withColumn("length", udf(lambda s: len(s), StringType()))

正確答案: C

說明：（僅 VCESoft 成員可見）

問題10

A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

B. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")

C. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

D. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

正確答案: A

說明：（僅 VCESoft 成員可見）

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