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NEW QUESTION: 1
Azure Machine Learningワークスペースには、real_estate_dataという名前のデータセットがあります。データセット内のデータのサンプルを次に示します。
自動機械学習を使用して、価格列を予測するための最適な回帰モデルを見つけたいと考えています。
Azure Machine Learning SDKを使用して、自動機械学習実験を構成する必要があります。
コードをどのように完成させる必要がありますか？回答するには、回答領域で適切なオプションを選択します。
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: training_data
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.
Box 2: validation_data
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class' ) Box 3: label_column_name label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Incorrect Answers:
X: The training features to use when fitting pipelines during an experiment. This setting is being deprecated. Please use training_data and label_column_name instead.
Y: The training labels to use when fitting pipelines during an experiment. This is the value your model will predict. This setting is being deprecated. Please use training_data and label_column_name instead.
X_valid: Validation features to use when fitting pipelines during an experiment.
If specified, then y_valid or sample_weight_valid must also be specified.
Y_valid: Validation labels to use when fitting pipelines during an experiment.
Both X_valid and y_valid must be specified together.
exclude_nan_labels: Whether to exclude rows with NaN values in the label. The default is True.
y_max: y_max (float)
Maximum value of y for a regression experiment. The combination of y_min and y_max are used to normalize test set metrics based on the input data range. If not specified, the maximum value is inferred from the data.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py
NEW QUESTION: 2
You need to implement event storage isolation and consistency.
Which settings should you use? To answer, drag the appropriate values to the correct operations. Each value
may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to
view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Scenario: Individual events must be immutable. Event data will be stored in Cosmos DB using the Document
API.
Box 1: Strong
Strong: Strong consistency offers a linearizability guarantee. The reads are guaranteed to return the most
recent committed version of an item. A client never sees an uncommitted or partial write. Users are always
guaranteed to read the latest committed write.
Box 2: Strong
Strong: Strong consistency offers a linearizability guarantee. The reads are guaranteed to return the most
recent committed version of an item. A client never sees an uncommitted or partial write. Users are always
guaranteed to read the latest committed write.
Box 3: Consistent
Azure Cosmos DB supports two indexing modes:
Consistent: If a container's indexing policy is set to Consistent, the index is updated synchronously as you
create, update or delete items. This means that the consistency of your read queries will be the consistency
configured for the account.
None: If a container's indexing policy is set to None, indexing is effectively disabled on that container. This is
commonly used when a container is used as a pure key-value store without the need for secondary indexes. It
can also help speeding up bulk insert operations.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/index-policy
NEW QUESTION: 3
A user is locked due to too many failed logon attempts.
Which SQL command can solve this problem?
Please choose the correct answer.
Choose one:
A. ALTER USER <user_name> DROP CONNECT ATTEMPTS
B. ALTER USER <username> RESET CONNECT ATTEMPTS
C. ALTER USER <user_name> ACCOUNT UNLOCK
D. ALTER USER <username> FORCE PASSWORD CHANGE AFTER ATTEMPTS
Answer: B