Associate-Developer-Apache-Sparkの無料問題集（179題）、Associate-Developer-Apache-Spark認定試験参考書

Databricks Associate-Developer-Apache-Spark 問題集

試験コード：Associate-Developer-Apache-Spark

試験名称：Databricks Certified Associate Developer for Apache Spark 3.0 Exam

最近更新時間：2024-11-15

問題と解答：全179問

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無料問題集Associate-Developer-Apache-Spark 資格取得

質問 1：
Which of the following describes a valid concern about partitioning?
A. A shuffle operation returns 200 partitions if not explicitly set.
B. The coalesce() method should be used to increase the number of partitions.
C. Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.
D. Short partition processing times are indicative of low skew.
E. No data is exchanged between executors when coalesce() is run.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
The code block shown below should return a single-column DataFrame with a column named consonant_ct that, for each row, shows the number of consonants in column itemName of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+ Code block:
itemsDf.select(__1__(__2__(__3__(__4__), "a|e|i|o|u|\s", "")).__5__("consonant_ct"))
A. 1. length
2. regexp_extract
3. upper
4. col("itemName")
5. as
B. 1. size
2. regexp_extract
3. lower
4. col("itemName")
5. alias
C. 1. lower
2. regexp_replace
3. length
4. "itemName"
5. alias
D. 1. size
2. regexp_replace
3. lower
4. "itemName"
5. alias
E. 1. length
2. regexp_replace
3. lower
4. col("itemName")
5. alias
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 3：
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", format="MM/dd/yyyy"))
B. transactionsDf.apply(from_unixtime(format="MM/dd/yyyy")).asColumn("transactionDateFormatted")
C. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate"))
D. transactionsDf.withColumn("transactionDateFormatted", from_unixtime("transactionDate", format="dd/MM/yyyy"))
E. transactionsDf.withColumnRenamed("transactionDate", "transactionDateFormatted", from_unixtime("transactionDateFormatted", format="MM/dd/yyyy"))
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
Sample of 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.drop([col("predError"), col("value")])
B. transactionsDf.drop(["predError", "value"])
C. transactionsDf.drop("predError", "value")
D. transactionsDf.drop(col("value"), col("predError"))
E. transactionsDf.drop(value, predError)
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Which of the following code blocks shows the structure of a DataFrame in a tree-like way, containing both column names and types?
A. itemsDf.rdd.printSchema()
B. spark.schema(itemsDf)
C. 1.print(itemsDf.columns)
2.print(itemsDf.types)
D. itemsDf.print.schema()
E. itemsDf.printSchema()
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Which of the following describes a narrow transformation?
A. A narrow transformation is an operation in which no data is exchanged across the cluster.
B. narrow transformation is an operation in which data is exchanged across partitions.
C. A narrow transformation is an operation in which data is exchanged across the cluster.
D. A narrow transformation is a process in which 32-bit float variables are cast to smaller float variables, like 16-bit or 8-bit float variables.
E. A narrow transformation is a process in which data from multiple RDDs is used.
正解：A
解説: (Topexam メンバーにのみ表示されます)

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Databricks Certified Associate Developer for Apache Spark 3.0 認定 Associate-Developer-Apache-Spark 試験問題:

1. Which of the following code blocks reads in the two-partition parquet file stored at filePath, making sure all columns are included exactly once even though each partition has a different schema?
Schema of first partition:
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)
Schema of second partition:
1.root
2. |-- transactionId: integer (nullable = true)
3. |-- predError: integer (nullable = true)
4. |-- value: integer (nullable = true)
5. |-- storeId: integer (nullable = true)
6. |-- rollId: integer (nullable = true)
7. |-- f: integer (nullable = true)
8. |-- tax_id: integer (nullable = false)

A) 1.nx = 0
2.for file in dbutils.fs.ls(filePath):
3. if not file.name.endswith(".parquet"):
4. continue
5. df_temp = spark.read.parquet(file.path)
6. if nx == 0:
7. df = df_temp
8. else:
9. df = df.union(df_temp)
10. nx = nx+1
11.df
B) spark.read.parquet(filePath, mergeSchema='y')
C) spark.read.option("mergeSchema", "true").parquet(filePath)
D) 1.nx = 0
2.for file in dbutils.fs.ls(filePath):
3. if not file.name.endswith(".parquet"):
4. continue
5. df_temp = spark.read.parquet(file.path)
6. if nx == 0:
7. df = df_temp
8. else:
9. df = df.join(df_temp, how="outer")
10. nx = nx+1
11.df
E) spark.read.parquet(filePath)

2. Which of the following describes the difference between client and cluster execution modes?

A) In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.
B) In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
C) In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
D) In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.
E) In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.

3. Which of the following describes how Spark achieves fault tolerance?

A) Spark builds a fault-tolerant layer on top of the legacy RDD data system, which by itself is not fault tolerant.
B) Due to the mutability of DataFrames after transformations, Spark reproduces them using observed lineage in case of worker node failure.
C) Spark helps fast recovery of data in case of a worker fault by providing the MEMORY_AND_DISK storage level option.
D) Spark is only fault-tolerant if this feature is specifically enabled via the spark.fault_recovery.enabled property.
E) If an executor on a worker node fails while calculating an RDD, that RDD can be recomputed by another executor using the lineage.

4. The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:

A) Spark will only apply the limit to threshold joins and not to other joins.
B) The passed limit has the wrong variable type.
C) The correct option to write configurations is through spark.config and not spark.conf.
D) spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 20)
E) Spark will only broadcast DataFrames that are much smaller than the default value.
F) The command is evaluated lazily and needs to be followed by an action.

5. Which of the following statements about lazy evaluation is incorrect?

A) Spark will fail a job only during execution, but not during definition.
B) Execution is triggered by transformations.
C) Lineages allow Spark to coalesce transformations into stages
D) Predicate pushdown is a feature resulting from lazy evaluation.
E) Accumulators do not change the lazy evaluation model of Spark.

質問と回答：

質問 # 1
正解： C

質問 # 2
正解： A

質問 # 3
正解： E

質問 # 4
正解： E

質問 # 5
正解： B