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NEW QUESTION: 1
A waiter earns a basic amount of 500 pounds per month regardless of her tips. How much did the waiter receive on tips during the month of May?
(1) On May, the waiter earned a total amount of money that was 150% of the basic.
(2) The waiter receives an average amount of 250 pounds on tips per month.
A. Statements (1) and (2) TAKEN TOGETHER are sufficient to answer the question, even though NEITHER statement BY ITSELF is sufficient.
B. Statement (1) BY ITSELF is sufficient to answer the question, but statement (2) by itself is not.
C. Either statement BY ITSELF is sufficient to answer the question.
D. Statements (1) and (2) TAKEN TOGETHER are NOT sufficient to answer the question, requiring more data pertaining to the problem.
E. Statement (2) BY ITSELF is sufficient to answer the question, but statement (1) by itself is not.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
We need to find the amount of money that the waiter earned on tips.
Statement (1) tells us that the total amount money earned is 150% of the basic, therefore the tips are 50% of 500 pounds, thus 250 pounds. This statement is sufficient.
Statement (2) is insufficient since the amount of tips earned on may can be over or under the average and so we can't pinpoint the exact amount.
NEW QUESTION: 2
Which change can correct inband access to the WLC?
A. enable the switch FastEthernetO/1 spanning-tree port-fast trunk
B. change the switch FastEthernetO/1 speed setting
C. change the switch FastEthernetO/1 trunk encapsulation
D. change the WLC configuration of NTP
E. change the switch FastEthernetO/1 duplex setting
F. change the WLC management interface VLAN
G. change the WLC management interface to use DHCP
H. browse to WLC via http://10.10.10.10
Answer: C
Explanation:
Explanation/Reference:
Explanation:
All of the other ports in the network are using the default VLAN (VLAN 1) but port 0/1, which connects to the WLC, is configured as a trunk, so VLAN 1 is not operational.
NEW QUESTION: 3
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: 4
You need to create a function that will use a SELECT statement in ProductsByProductType.sql. Which code segment should you use to complete the function?
A. Option D
B. Option C
C. Option A
D. Option B
Answer: D