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DB-MLA 試験練習問題

Databricks Certified Machine Learning Associate

DB-MLA の PDF 練習問題。資料の言語は 英語です。購入はこの試験のみで、自動更新はありません。

試験概要

DB-MLA は「Databricks Certified Machine Learning Associate」の認定試験です。試験形式、時間、対応言語は以下をご確認ください。最新の出題範囲や受験条件は公式サイトをご参照ください。

試験コード
DB-MLA
公式の試験・資格名
Databricks Certified Machine Learning Associate
試験形式
Proctored multiple-choice assessment; see the guide for scored and any additional unscored items
試験時間
90 分
受験可能な言語
英語、日本語、ポルトガル語（ブラジル）、韓国語

公式試験の対応言語と練習問題ファイルの言語は異なります。試験の版と言語は公式の予約ページでご確認ください。

公式情報： 公式サイト
情報確認日：2026-10-20

対応言語

  • 英語
  • 簡体字中国語
購入前にお試し

最初の 20 問を無料プレビュー

全 74 問

題庫の元の順序で表示します。回答欄を開くと、正解と収録されている解説を確認できます。

問題の言語：英語。問題文・選択肢・正解・解説は元の題庫の言語で表示します。

免責事項：ExamPico の問題はすべてインターネット上の情報を整理したもので、学習参考用として提供しています。

問題 6

Question#6Topic1

A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.
Which of the following lines of code can the data scientist run to accomplish the task?

  1. A. spark_df.summary ()
  2. B. spark_df.stats()
  3. C. spark_df.describe().head()
  4. D. spark_df.printSchema()
  5. E. spark_df.toPandas()
正解を表示

正解

A

問題 7

Question#7Topic1

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

  1. A. One-hot encoding is not supported by most machine learning libraries.
  2. B. One-hot encoding is dependent on the target variable’s values which differ for each application.
  3. C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
  4. D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
  5. E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
正解を表示

正解

E

問題 8

Question#8Topic1

A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model by comparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model.
Which of the following possible explanations for this difference is invalid?

  1. A. The second model is much more accurate than the first model
  2. B. The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE
  3. C. The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE
  4. D. The first model is much more accurate than the second model
  5. E. The RMSE is an invalid evaluation metric for regression problems
正解を表示

正解

B

問題 9

Question#9Topic1

A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
• 10.0
• 12.0
• 17.0
Which of the following values represents the overall cross-validation root-mean-squared error?

  1. A. 13.0
  2. B. 17.0
  3. C. 12.0
  4. D. 39.0
  5. E. 10.0
正解を表示

正解

A

問題 10

Question#10Topic1

A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:
Question illustration
A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

  1. A. The model will take longer to train for each unique combination of hyperparameter values
  2. B. The feature engineering stages will be computed using validation data
  3. C. The cross-validation process will no longer be parallelizable
  4. D. The cross-validation process will no longer be reproducible
  5. E. The model will be refit one more per cross-validation fold
正解を表示

正解

B

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