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

問題1

A. itemsDf.filter(!col('supplier').contains('X')).select(col('supplier')).unique()

B. itemsDf.select(~col('supplier').contains('X')).distinct()

C. itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()

D. itemsDf.filter(col(supplier).not_contains('X')).select(supplier).distinct()

E. itemsDf.filter(not(col('supplier').contains('X'))).select('supplier').unique()

正確答案: C

說明：（僅 Fast2test 成員可見）

問題2

Which of the following code blocks concatenates rows of DataFrames transactionsDf and transactionsNewDf, omitting any duplicates?

A. transactionsDf.union(transactionsNewDf).unique()

B. transactionsDf.union(transactionsNewDf).distinct()

C. transactionsDf.join(transactionsNewDf, how="union").distinct()

D. spark.union(transactionsDf, transactionsNewDf).distinct()

E. transactionsDf.concat(transactionsNewDf).unique()

正確答案: B

說明：（僅 Fast2test 成員可見）

問題3

Which of the following statements about reducing out-of-memory errors is incorrect?

A. Concatenating multiple string columns into a single column may guard against out-of-memory errors.

B. Reducing partition size can help against out-of-memory errors.

C. Limiting the amount of data being automatically broadcast in joins can help against out-of-memory errors.

D. Setting a limit on the maximum size of serialized data returned to the driver may help prevent out-of-memory errors.

E. Decreasing the number of cores available to each executor can help against out-of-memory errors.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題4

Which of the following describes slots?

A. Slots are dynamically created and destroyed in accordance with an executor's workload.

B. A Java Virtual Machine (JVM) working as an executor can be considered as a pool of slots for task execution.

C. To optimize I/O performance, Spark stores data on disk in multiple slots.

D. A slot is always limited to a single core.
Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.

正確答案: B

說明：（僅 Fast2test 成員可見）

問題5

Which of the following code blocks produces the following output, given DataFrame transactionsDf?
Output:
1.root
2. |-- transactionId: integer (nullable = true)
3. |-- predError: integer (nullable = true)
4. |-- value: integer (nullable = true)
5. |-- storeId: integer (nullable = true)
6. |-- productId: integer (nullable = true)
7. |-- f: integer (nullable = true)
DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.+-------------+---------+-----+-------+---------+----+

A. transactionsDf.rdd.formatSchema()

B. print(transactionsDf.schema)

C. transactionsDf.printSchema()

D. transactionsDf.schema.print()

E. transactionsDf.rdd.printSchema()

正確答案: C

說明：（僅 Fast2test 成員可見）

問題6

Which of the following code blocks returns a DataFrame with an added column to DataFrame transactionsDf that shows the unix epoch timestamps in column transactionDate as strings in the format month/day/year in column transactionDateFormatted?
Excerpt of DataFrame transactionsDf:

A. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))

B. transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))

C. transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")

D. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))

E. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="MM/dd/yyyy"))

正確答案: E

說明：（僅 Fast2test 成員可見）

問題7

Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?

A. transactionsDf.filter((col("productId")==3) | (col("productId")<1))

B. transactionsDf.filter(productId==3 or productId<1)

C. transactionsDf.filter(col("productId")==3 | col("productId")<1)

D. transactionsDf.where("productId"=3).or("productId"<1))

E. transactionsDf.filter((col("productId")==3) or (col("productId")<1))

正確答案: A

說明：（僅 Fast2test 成員可見）

問題8

The code block displayed below contains an error. The code block should return the average of rows in column value grouped by unique storeId. Find the error.
Code block:
transactionsDf.agg("storeId").avg("value")

A. All column names should be wrapped in col() operators.

B. "storeId" and "value" should be swapped.

C. The avg("value") should be specified as a second argument to agg() instead of being appended to it.

D. agg should be replaced by groupBy.

E. Instead of avg("value"), avg(col("value")) should be used.

正確答案: D

說明：（僅 Fast2test 成員可見）

問題9

The code block displayed below contains an error. The code block is intended to perform an outer join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively.
Find the error.
Code block:
transactionsDf.join(itemsDf, [itemsDf.itemId, transactionsDf.productId], "outer")

A. The join type needs to be appended to the join() operator, like join().outer() instead of listing it as the last argument inside the join() call.

B. The "outer" argument should be eliminated from the call and join should be replaced by joinOuter.

C. The term [itemsDf.itemId, transactionsDf.productId] should be replaced by itemsDf.col("itemId") == transactionsDf.col("productId").

D. The term [itemsDf.itemId, transactionsDf.productId] should be replaced by itemsDf.itemId == transactionsDf.productId.

E. The "outer" argument should be eliminated, since "outer" is the default join type.

正確答案: D

說明：（僅 Fast2test 成員可見）

問題10

Which of the following code blocks performs an inner join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively, excluding columns value and storeId from DataFrame transactionsDf and column attributes from DataFrame itemsDf?

A. transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"),
"transactionsDf.productId==itemsDf.itemId")

B. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
2.itemsDf.createOrReplaceTempView('itemsDf')
3.
4.statement = """
5.SELECT * FROM transactionsDf
6.INNER JOIN itemsDf
7.ON transactionsDf.productId==itemsDf.itemId
8."""
9.spark.sql(statement).drop("value", "storeId", "attributes")

C. 1.transactionsDf \
2. .drop(col('value'), col('storeId')) \
3. .join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))

D. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
2.itemsDf.createOrReplaceTempView('itemsDf')
3.
4.spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes")

E. transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId)

正確答案: B

說明：（僅 Fast2test 成員可見）

問題11

The code block shown below should add column transactionDateForm to DataFrame transactionsDf. The column should express the unix-format timestamps in column transactionDate as string type like Apr 26 (Sunday). Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, from_unixtime(__3__, __4__))

A. 1. withColumnRenamed
2. "transactionDate"
3. "transactionDateForm"
4. "MM d (EEE)"

B. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MMM d (EEEE)"

C. 1. withColumn
2. "transactionDateForm"
3. "MMM d (EEEE)"
4. "transactionDate"

D. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MM d (EEE)"

E. 1. select
2. "transactionDate"
3. "transactionDateForm"
4. "MMM d (EEEE)"

正確答案: B

說明：（僅 Fast2test 成員可見）

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