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Associate-Developer-Apache-Spark日本語受験攻略、Associate-Developer-Apache-Spark復習攻略問題

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Databricks Associate-Developer-Apache-Spark 認定試験は、Apache Spark技術に関するスキルと専門知識を検証するための絶好の機会です。これはまた、組織が従業員がApache Spark技術を扱うために必要なスキルを持っているかどうかを確認するための素晴らしい方法でもあります。この認定試験は2年間有効であり、その後は候補者が更新する必要があります。全体的に、この認定試験は、ビッグデータと分析の分野でキャリアの展望を向上させたいプロフェッショナルにとって、貴重な資格となります。

Databricks Associate-Developer-Apache-Spark 認定試験は、Databricks を使用した Apache Spark 開発の専門知識を証明するのに価値のある資格です。この認定試験では、Apache Spark 関連の幅広いトピックがカバーされ、Databricks を使用して Apache Spark アプリケーションを開発・展開するための知識や能力がテストされます。試験に合格することで、候補者はデジタルバッジや証明書を取得し、プロフェッショナルプロファイルに表示することができ、求人市場で差別化することができます。

>> Associate-Developer-Apache-Spark日本語受験攻略 <<

Associate-Developer-Apache-Spark復習攻略問題 & Associate-Developer-Apache-Spark日本語試験対策

最短時間でAssociate-Developer-Apache-Spark試験に合格すると、Xhs1991すべての受験者の声になります。しかし、圧倒的な学習教材で最も価値のある情報を選択する方法は、すべての試験官にとって頭痛の種です。絶え間ない努力の後、Associate-Developer-Apache-Spark学習ガイドは誰もが期待するものです。当社の専門家は、コンテンツを簡素化し、お客様の重要なポイントを把握するだけでなく、Associate-Developer-Apache-Spark準備資料を簡単な言語に再コンパイルしました。レジャー学習体験と、今後のAssociate-Developer-Apache-Spark 試験Databricks Certified Associate Developer for Apache Spark 3.0 Exam合格できます。

Databricks Associate-Developer-Apache-Spark認定試験は、Sparkアーキテクチャ、Spark SQL、Sparkストリーム、機械学習、データフレームなどのトピックをカバーしています。この試験は、複数選択肢の問題から構成され、候補者がSparkを使用して現実のデータ処理問題を解決するための知識とスキルを証明することを要求します。この認定は業界で高い評価を受けており、データ処理と分析にApache Sparkを使用する主要な組織によって認められています。認定を取得することで、開発者はキャリアを進め、スキルに対する認識を得ることができ、収入を増やすことができます。

Databricks Certified Associate Developer for Apache Spark 3.0 Exam 認定 Associate-Developer-Apache-Spark 試験問題 (Q89-Q94):

質問 # 89
The code block displayed below contains an error. The code block should return all rows of DataFrame transactionsDf, but including only columns storeId and predError. Find the error.
Code block:
spark.collect(transactionsDf.select("storeId", "predError"))

A. Instead of collect, collectAsRows needs to be called.
B. Columns storeId and predError need to be represented as a Python list, so they need to be wrapped in brackets ([]).
C. Instead of select, DataFrame transactionsDf needs to be filtered using the filter operator.
D. The collect method is not a method of the SparkSession object.
E. The take method should be used instead of the collect method.

正解：D

解説：
Explanation
Correct code block:
transactionsDf.select("storeId", "predError").collect()
collect() is a method of the DataFrame object.
More info: pyspark.sql.DataFrame.collect - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

質問 # 90
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?

A. spark.read().schema(fileSchema).parquet(filePath)
B. spark.read.schema(fileSchema).format("parquet").load(filePath)
C. spark.read().schema(fileSchema).format(parquet).load(filePath)
D. spark.read.schema("fileSchema").format("parquet").load(filePath)
E. spark.read.schema(fileSchema).open(filePath)

正解：B

解説：
Explanation
Pay attention here to which variables are quoted. fileSchema is a variable and thus should not be in quotes.
parquet is not a variable and therefore should be in quotes.
SparkSession.read (here referenced as spark.read) returns a DataFrameReader which all subsequent calls reference - the DataFrameReader is not callable, so you should not use parentheses here.
Finally, there is no open method in PySpark. The method name is load.
Static notebook | Dynamic notebook: See test 1

質問 # 91
Which of the following statements about RDDs is incorrect?

A. RDDs are immutable.
B. The high-level DataFrame API is built on top of the low-level RDD API.
C. RDDs are great for precisely instructing Spark on how to do a query.
D. An RDD consists of a single partition.
E. RDD stands for Resilient Distributed Dataset.

正解：D

解説：
Explanation
An RDD consists of a single partition.
Quite the opposite: Spark partitions RDDs and distributes the partitions across multiple nodes.

質問 # 92
Which of the following code blocks returns a DataFrame where columns predError and productId are removed from DataFrame transactionsDf?
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("predError", "productId", "associateId")
B. transactionsDf.drop(["predError", "productId", "associateId"])
C. transactionsDf.withColumnRemoved("predError", "productId")
D. transactionsDf.dropColumns("predError", "productId", "associateId")
E. transactionsDf.drop(col("predError", "productId"))

正解：D

解説：
Explanation
The key here is to understand that columns that are passed to DataFrame.drop() are ignored if they do not exist in the DataFrame. So, passing column name associateId to transactionsDf.drop() does not have any effect.
Passing a list to transactionsDf.drop() is not valid. The documentation (link below) shows the call structure as DataFrame.drop(*cols). The * means that all arguments that are passed to DataFrame.drop() are read as columns. However, since a list of columns, for example ["predError",
"productId", "associateId"] is not a column, Spark will run into an error.
More info: pyspark.sql.DataFrame.drop - PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1

質問 # 93
Which of the following is a problem with using accumulators?

A. Only numeric values can be used in accumulators.
B. Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
C. Accumulators do not obey lazy evaluation.
D. Only unnamed accumulators can be inspected in the Spark UI.
E. Accumulator values can only be read by the driver, but not by executors.

正解：E

解説：
Explanation
Accumulator values can only be read by the driver, but not by executors.
Correct. So, for example, you cannot use an accumulator variable for coordinating workloads between executors. The typical, canonical, use case of an accumulator value is to report data, for example for debugging purposes, back to the driver. For example, if you wanted to count values that match a specific condition in a UDF for debugging purposes, an accumulator provides a good way to do that.
Only numeric values can be used in accumulators.
No. While pySpark's Accumulator only supports numeric values (think int and float), you can define accumulators for custom types via the AccumulatorParam interface (documentation linked below).
Accumulators do not obey lazy evaluation.
Incorrect - accumulators do obey lazy evaluation. This has implications in practice: When an accumulator is encapsulated in a transformation, that accumulator will not be modified until a subsequent action is run.
Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
Wrong. A concern with accumulators is in fact that under certain conditions they can run for each task more than once. For example, if a hardware failure occurs during a task after an accumulator variable has been increased but before a task has finished and Spark launches the task on a different worker in response to the failure, already executed accumulator variable increases will be repeated.
Only unnamed accumulators can be inspected in the Spark UI.
No. Currently, in PySpark, no accumulators can be inspected in the Spark UI. In the Scala interface of Spark, only named accumulators can be inspected in the Spark UI.
More info: Aggregating Results with Spark Accumulators | Sparkour, RDD Programming Guide - Spark 3.1.2 Documentation, pyspark.Accumulator - PySpark 3.1.2 documentation, and pyspark.AccumulatorParam - PySpark 3.1.2 documentation

質問 # 94
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