Databricks Associate-Developer-Apache-Spark 専門知識 しかし準備しなければならないのですから、落ち着かない心理になりました、Associate-Developer-Apache-Spark学習教材の包括的な理解を得るために、Associate-Developer-Apache-Spark試験問題のデモを無料でダウンロードする場合は、まず製品の紹介をご覧ください、Associate-Developer-Apache-Spark証明書の価値のため、ますます多くの人々は、Associate-Developer-Apache-Spark認定試験を受けることを選びます、Associate-Developer-Apache-Spark無料トレーニングPDF版は私たちの認定専門家によってテストされて認可するものです、あなたは無料デモから問題の正確性をチェックできます、また、99％から100％の高い合格率により、Associate-Developer-Apache-Spark試験は非常に簡単です、一部の試験受験者は、有用なAssociate-Developer-Apache-Sparkの実際のテストを切望しているため、当社の製品は、効率的な練習資料が非常に不足しているお客様やその他のお客様に役立ちます。
なにはさておき、あせらぬことだ、いちおうこれ看板だからね、ほんとに決まってんだろ、お支払い後、システムからAssociate-Developer-Apache-Spark最新ダンプのダウンロードリンク、アカウント、パスワードを含むメールが送信されます、第七監獄に戻れないならば、せいぜいこの地の美酒を楽しむのみだ。
Associate-Developer-Apache-Spark問題集を今すぐダウンロード
笛は柏木（かしわぎ）の大納言が夢に出て伝える人を夕霧へ暗示した形見のもので、非Associate-Developer-Apache-Spark受験記常によい音（ね）の出るものであると六条院がお愛しになったものを、右大将へ贈るのはこの美しい機会以外にないと思い、薫のためにこの人が用意してきたのであるらしい。
子宮も卵巣も正常に働いているのに、経血を出す術が無い、───自分の間抜けっぷりにAssociate-Developer-Apache-Spark日本語版参考資料、さすがに眩暈がして来る 思わずがっくり項垂れて掌で顔を覆うと、彼女は大きく咳払いをした、いえ 今までにすすめた連中だって、家柄や財産で決めた男なんて全然いないぞ。
全てを投げ出そうかとも思い悩んだんだ、無茶はいけない―そう思っていても、Associate-Developer-Apache-Sparkテスト問題集体が動いてしまう、親の都合で翻弄される自分が小さく、惨めに思えて、力の限り否定した、ううううう 必死に目で二本でいいからもっと激しくしてと訴える。
その直後ちょくご、信秀のぶひでの軍ぐんが城下じょうかに乱入らんにゅうしAssociate-Developer-Apache-Spark問題と解答、息いきつく間まもなく城下じょうかの民家みんか、寺てら、武家ぶけ屋敷やしきを手てあたり次第しだいに焼やきはじめた、下着これから洗濯どうしよう。
が、一羽の雄鶏の墨画（すみゑ）は著しい個性を示してゐた、電車の乗り方も、バスの時刻Associate-Developer-Apache-Spark専門知識も、リズが全部教えてくれたわ へぇ、リズって言うのか、わからずに焦っている、二時から三時までは、椅子が六脚しかない小会議室で、二階堂からプレゼンのレクチャーを受けた。
これは、この入り口を通過するだけです、弊社のAssociate-Developer-Apache-Spark勉強ガイドの資料を勉強する限り、Associate-Developer-Apache-Spark認定試験に合格できることを保証します、面を被ったように荒れた気性を隠し、お紺は何食わぬ顔で天 嵐の夜道を歩きながらお紺は気を静めていった。
Associate-Developer-Apache-Spark試験の準備方法｜検証するAssociate-Developer-Apache-Spark 専門知識試験｜正確的なDatabricks Certified Associate Developer for Apache Spark 3.0 Exam 受験記
周りからはそう見られてるんだよな、逆もまた然り、で、でも、おじいちゃんのご友人とAssociate-Developer-Apache-Spark専門知識いうことですけど私までご招待いただいてよかったんでしょうか 祖父も久しぶりに会う、ずいぶん年下の友人らしい、控えめに男が尋ねれば、トオルは、弱々しく頷いてみせた。
そ、そうだよ、だたの友人だよ、これでお役御免とは、とても可哀そう、ただし、レポhttps://www.tech4exam.com/databricks-certified-associate-developer-for-apache-spark-3.0-exam-japanese-14198.htmlートでは説明していないがブログで説明している上級ユーザーを調査していることに気付くと、データが理解しやすくなります、僕は自分の感じていることを正直に書いた。
Databricks Certified Associate Developer for Apache Spark 3.0 Exam問題集を今すぐダウンロード
質問 43
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. A narrow transformation is a process in which data from multiple RDDs is used.
- C. narrow transformation is an operation in which data is exchanged across partitions.
- D. A narrow transformation is an operation in which data is exchanged across the cluster.
- E. 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.
正解: A
解説:
Explanation
A narrow transformation is an operation in which no data is exchanged across the cluster.
Correct! In narrow transformations, no data is exchanged across the cluster, since these transformations do not require any data from outside of the partition they are applied on. Typical narrow transformations include filter, drop, and coalesce.
A narrow transformation is an operation in which data is exchanged across partitions.
No, that would be one definition of a wide transformation, but not of a narrow transformation. Wide transformations typically cause a shuffle, in which data is exchanged across partitions, executors, and the cluster.
A narrow transformation is an operation in which data is exchanged across the cluster.
No, see explanation just above this one.
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.
No, type conversion has nothing to do with narrow transformations in Spark.
A narrow transformation is a process in which data from multiple RDDs is used.
No. A resilient distributed dataset (RDD) can be described as a collection of partitions. In a narrow transformation, no data is exchanged between partitions. Thus, no data is exchanged between RDDs.
One could say though that a narrow transformation and, in fact, any transformation results in a new RDD being created. This is because a transformation results in a change to an existing RDD (RDDs are the foundation of other Spark data structures, like DataFrames). But, since RDDs are immutable, a new RDD needs to be created to reflect the change caused by the transformation.
More info: Spark Transformation and Action: A Deep Dive | by Misbah Uddin | CodeX | Medium
質問 44
The code block displayed below contains an error. The code block below is intended to add a column itemNameElements to DataFrame itemsDf that includes an array of all words in column itemName. Find the error.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-------------------+
2.|itemId|itemName |supplier |
3.+------+----------------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |YetiX |
6.|3 |Outdoors Backpack |Sports Company Inc.|
7.+------+----------------------------------+-------------------+
Code block:
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
itemsDf.withColumnRenamed("itemNameElements", split("itemName"))
- A. Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument "
" needs to be passed to the split method. - B. Operator withColumnRenamed needs to be replaced with operator withColumn and the split method needs to be replaced by the splitString method.
- C. All column names need to be wrapped in the col() operator.
- D. Operator withColumnRenamed needs to be replaced with operator withColumn and a second argument
"," needs to be passed to the split method. - E. The expressions "itemNameElements" and split("itemName") need to be swapped.
正解: A
解説:
Explanation
Correct code block:
itemsDf.withColumn("itemNameElements", split("itemName"," "))
Output of code block:
+------+----------------------------------+-------------------+------------------------------------------+
|itemId|itemName |supplier |itemNameElements |
+------+----------------------------------+-------------------+------------------------------------------+
|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in, the, Snow]|
|2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress] |
|3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |
+------+----------------------------------+-------------------+------------------------------------------+ The key to solving this question is that the split method definitely needs a second argument here (also look at the link to the documentation below). Given the values in column itemName in DataFrame itemsDf, this should be a space character " ". This is the character we need to split the words in the column.
More info: pyspark.sql.functions.split - PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1
質問 45
Which of the following describes Spark's standalone deployment mode?
- A. Standalone mode uses only a single executor per worker per application.
- B. Standalone mode is how Spark runs on YARN and Mesos clusters.
- C. Standalone mode is a viable solution for clusters that run multiple frameworks, not only Spark.
- D. Standalone mode means that the cluster does not contain the driver.
- E. Standalone mode uses a single JVM to run Spark driver and executor processes.
正解: A
解説:
Explanation
Standalone mode uses only a single executor per worker per application.
This is correct and a limitation of Spark's standalone mode.
Standalone mode is a viable solution for clusters that run multiple frameworks.
Incorrect. A limitation of standalone mode is that Apache Spark must be the only framework running on the cluster. If you would want to run multiple frameworks on the same cluster in parallel, for example Apache Spark and Apache Flink, you would consider the YARN deployment mode.
Standalone mode uses a single JVM to run Spark driver and executor processes.
No, this is what local mode does.
Standalone mode is how Spark runs on YARN and Mesos clusters.
No. YARN and Mesos modes are two deployment modes that are different from standalone mode. These modes allow Spark to run alongside other frameworks on a cluster. When Spark is run in standalone mode, only the Spark framework can run on the cluster.
Standalone mode means that the cluster does not contain the driver.
Incorrect, the cluster does not contain the driver in client mode, but in standalone mode the driver runs on a node in the cluster.
More info: Learning Spark, 2nd Edition, Chapter 1
質問 46
The code block displayed below contains an error. The code block should return a new DataFrame that only contains rows from DataFrame transactionsDf in which the value in column predError is at least 5. Find the error.
Code block:
transactionsDf.where("col(predError) >= 5")
- A. The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").
- B. The argument to the where method cannot be a string.
- C. Instead of >=, the SQL operator GEQ should be used.
- D. Instead of where(), filter() should be used.
- E. The argument to the where method should be "predError >= 5".
正解: E
解説:
Explanation
The argument to the where method cannot be a string.
It can be a string, no problem here.
Instead of where(), filter() should be used.
No, that does not matter. In PySpark, where() and filter() are equivalent.
Instead of >=, the SQL operator GEQ should be used.
Incorrect.
The expression returns the original DataFrame transactionsDf and not a new DataFrame. To avoid this, the code block should be transactionsDf.toNewDataFrame().where("col(predError) >= 5").
No, Spark returns a new DataFrame.
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/spark_practice_tests_code/#1/27.html ,
https://bit.ly/sparkpracticeexams_import_instructions)
質問 47
Which of the following code blocks concatenates rows of DataFrames transactionsDf and transactionsNewDf, omitting any duplicates?
- A. transactionsDf.union(transactionsNewDf).unique()
- B. transactionsDf.join(transactionsNewDf, how="union").distinct()
- C. spark.union(transactionsDf, transactionsNewDf).distinct()
- D. transactionsDf.union(transactionsNewDf).distinct()
- E. transactionsDf.concat(transactionsNewDf).unique()
正解: D
解説:
Explanation
DataFrame.unique() and DataFrame.concat() do not exist and union() is not a method of the SparkSession. In addition, there is no union option for the join method in the DataFrame.join() statement.
More info: pyspark.sql.DataFrame.union - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
質問 48
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