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NEW QUESTION: 1
DRAG DROP
Drag and drop each step in the performance-monitoring configuration process on the left into the correct order on the right.

Answer:
Explanation:

NEW QUESTION: 2
You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Datiabricks and PolyBase in Azure Synapse Analytics.
You need to recommend a Stream Analytics data output format to ensure that the queries from Databricks and PolyBase against the files encounter the fewest possible errors. The solution must ensure that the tiles can be queried quickly and that the data type information is retained.
What should you recommend?
A. JSON
B. CSV
C. Parquet
D. Avro
Answer: D
Explanation:
The Avro format is great for data and message preservation. Avro schema with its support for evolution is essential for making the data robust for streaming architectures like Kafka, and with the metadata that schema provides, you can reason on the data. Having a schema provides robustness in providing meta-data about the data stored in Avro records which are self- documenting the data. References: http://cloudurable.com/blog/avro/index.html

NEW QUESTION: 3
Azure Machine Learning Studioを使用して実験を作成しています。
評価のためにデータを4つのサブセットに分割する必要があります。データには高度な欠損値があります。
分析用のデータを準備する必要があります。
実験を作成するための適切な方法を選択する必要があります。
どの3つのモジュールを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
注：回答の選択肢の複数の順序が正しいです。選択した正しい注文のいずれかのクレジットを受け取ります。

Answer:
Explanation:

Explanation:
The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
Incorrect Answers:
Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
Missing Value Scrubber: The Missing Values Scrubber module is deprecated.
Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data