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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題:

1. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
B) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
C) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
D) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

2. 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").distinct().orderBy(df["Name"])
B) df.select("Name").orderBy(df["Name"].asc())
C) df.select("Name").distinct()
D) df.select("Name").distinct().orderBy(df["Name"].desc())

3. 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.dropField("age").withColumn("tax", col("salary") * 0.1)
C) employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
D) employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")

4. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("overwrite").parquet("/data/output")
B) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
C) df.write.partitionBy("country").parquet("/data/output")
D) df.write.mode("append").partitionBy("country").parquet("/data/output")

5. 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.dropFields("name", "email", "birthdate")
C) df_user_non_pii = df_user.remove("name", "email", "birthdate")
D) df_user_non_pii = df_user.select("name", "email", "birthdate")

問題與答案：