Databricks-Machine-Learning-Associate学習資料、Databricks-Machine-Learning-Associate入門知識

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Databricks-Machine-Learning-Associate入門知識、Databricks-Machine-Learning-Associate模擬試験

今の多士済々な社会の中で、IT専門人士はとても人気がありますが、競争も大きいです。だからいろいろな方は試験を借って、自分の社会の地位を固めたいです。Databricks-Machine-Learning-Associate認定試験はDatabricksの中に重要な認証試験の一つですが、JpshikenにIT業界のエリートのグループがあって、彼達は自分の経験と専門知識を使ってDatabricks Databricks-Machine-Learning-Associate「Databricks Certified Machine Learning Associate Exam」認証試験に参加する方に対して問題集を研究続けています。

Databricks Databricks-Machine-Learning-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	ML ワークフロー: このトピックは、探索的データ分析、特徴エンジニアリング、トレーニング、評価、選択に焦点を当てています。
トピック 2	Databricks Machine Learning: AutoML、Databricks Runtime、Feature Store、MLflow のサブトピックをカバーします。
トピック 3	Spark ML: 分散 ML の概念について説明します。さらに、このトピックでは、Spark ML モデリング API、Hyperopt、Pandas API、Pandas UDF、関数 API についても説明します。
トピック 4	ML モデルのスケーリング: このトピックでは、モデルの配布とアンサンブルの配布について説明します。

Databricks Certified Machine Learning Associate Exam 認定 Databricks-Machine-Learning-Associate 試験問題 (Q25-Q30):

質問 # 25
A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
Which of the following lines of code can the data scientist run to accomplish the task?

A. This task cannot be accomplished in a single line of code.
B. dbutils.data.summarize (spark_df)
C. spark_df.summary()
D. dbutils.data(spark_df).summarize()
E. spark_df.describe()

正解：B

解説：
To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility function dbutils.data.summarize can be used. This function provides a comprehensive summary, including visual histograms.
Correct code:
dbutils.data.summarize(spark_df)
Other options like spark_df.describe() and spark_df.summary() provide textual statistical summaries but do not include visual histograms.
Reference:
Databricks Utilities Documentation

質問 # 26
A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.
In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

A. When the entire data can fit on each core
B. When the data is particularly wide in shape
C. When the data is particularly long in shape
D. When the model is unable to be parallelized
E. When the tuning process in randomized

正解：A

解説：
Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.
Reference:
Parallel Computing Concepts

質問 # 27
A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.
They have developed this code block to accomplish this task:

The code block is returning an error.
Which of the following adjustments does the data scientist need to make to accomplish this task?

A. They need to specify the method parameter to the OneHotEncoder.
B. They need to remove the line with the fit operation.
C. They need to use VectorAssembler prior to one-hot encoding the features.
D. They need to use Stringlndexer prior to one-hot encodinq the features.

正解：D

解説：
The OneHotEncoder in Spark ML requires numerical indices as inputs rather than string labels. Therefore, you need to first convert the string columns to numerical indices using StringIndexer. After that, you can apply OneHotEncoder to these indices.
Corrected code:
from pyspark.ml.feature import StringIndexer, OneHotEncoder # Convert string column to index indexers = [StringIndexer(inputCol=col, outputCol=col+"_index") for col in input_columns] indexer_model = Pipeline(stages=indexers).fit(features_df) indexed_features_df = indexer_model.transform(features_df) # One-hot encode the indexed columns ohe = OneHotEncoder(inputCols=[col+"_index" for col in input_columns], outputCols=output_columns) ohe_model = ohe.fit(indexed_features_df) ohe_features_df = ohe_model.transform(indexed_features_df) Reference:
PySpark ML Documentation

質問 # 28
A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of including pipeline as the estimator in the cross-validation process rather than rfr as the estimator?

A. The process will leak data prep information from the validation sets to the training sets for each model
B. The process will be unable to parallelize tuning due to the distributed nature of pipeline
C. The process will leak data from the training set to the test set during the evaluation phase
D. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode

正解：D

解説：
Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.
If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.
Reference:
Databricks documentation on cross-validation: Cross Validation

質問 # 29
A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?

A. When the new solution requires the use of fewer feature variables than the original model
B. When the new solution's models have an average latency that is larger than the size of the original model
C. When the new solution's models have an average size that is larger than the size of the original model
D. When the new solution requires that each model computes a prediction for every record
E. When the new solution requires if-else logic determining which model to use to compute each prediction

正解：D

解説：
If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.
In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.
Reference:
Model Ensemble Techniques

質問 # 30
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