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NEW QUESTION: 1
At what stage of the Fundamental Test Process do testers write the steps of the test procedures?
A. Test implementation and execution
B. Test closure activities
C. Test planning and control
D. Evaluating exit criteria and reporting
Answer: A
NEW QUESTION: 2
After you recommend Amazon Redshift to a client as an alternative solution to paying data warehouses to analyze his data, your client asks you to explain why you are recommending Redshift. Which of the following would be a reasonable response to his request?
A. It has high performance at scale as data and query complexity grows.
B. It prevents reporting and analytic processing from interfering with the performance of OLTP workloads.
C. You don't have the administrative burden of running your own data warehouse and dealing with setup, durability, monitoring, scaling, and patching.
D. All answers listed are a reasonable response to his question
Answer: D
Explanation:
Amazon Redshift delivers fast query performance by using columnar storage technology to improve I/O efficiency and parallelizing queries across multiple nodes. Redshift uses standard PostgreSQL JDBC and
ODBC drivers, allowing you to use a wide range of familiar SQL clients. Data load speed scales linearly with cluster size, with integrations to Amazon S3, Amazon DynamoDB, Amazon Elastic MapReduce,
Amazon Kinesis or any SSH-enabled host.
AWS recommends Amazon Redshift for customers who have a combination of needs, such as:
High performance at scale as data and query complexity grows
Desire to prevent reporting and analytic processing from interfering with the performance of OLTP workloads
Large volumes of structured data to persist and query using standard SQL and existing BI tools
Desire to the administrative burden of running one's own data warehouse and dealing with setup, durability, monitoring, scaling and patching
Reference: https://aws.amazon.com/running_databases/#redshift_anchor
NEW QUESTION: 3
TSVファイルのセットを含むAzureBLOBコンテナーがあります。 Azure BLOBコンテナーは、Azure MachineLearningサービスワークスペースのデータストアとして登録されます。各TSVファイルは同じデータスキーマを使用します。
すべてのTSVファイルのデータをまとめて集約し、Azure Machine Learning SDK for Pythonを使用して、集約されたデータをデータセットとしてAzure MachineLearningワークスペースに登録することを計画しています。
次のコードを実行します。
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: No
FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.
Box 2: Yes
to_path() gets a list of file paths for each file stream defined by the dataset.
Box 3: Yes
TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.
TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset