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NEW QUESTION: 1
You are building a pipeline to process time-series dat
a. Which Google Cloud Platform services should you put in boxes 1,2,3, and 4?

A. Cloud Pub/Sub, Cloud Storage, BigQuery, Cloud Bigtable
B. Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable, BigQuery
C. Firebase Messages, Cloud Pub/Sub, Cloud Spanner, BigQuery
D. Cloud Pub/Sub, Cloud Dataflow, Cloud Datastore, BigQuery
Answer: B
Explanation:
Reference:
https://cloud.google.com/solutions/correlating-time-series-dataflow

NEW QUESTION: 2
After opening a case with the Hitachi Data Systems technical support center, you have been asked to collect error information. How do you send the collected data to the technical support center?
A. Upload to https://tuf.hds.com.
B. Upload to ftp://ftp.hds.com.
C. Send an email to [email protected].
D. Upload to http://www.hds.com.
Answer: A

NEW QUESTION: 3
Which two options are advantages of using the Cisco IOS Device Sensor as compared to other profiling probes'? (Choose two.)
A. provides DHCP information to a Cisco ISE server without using an IP helper address
B. reduces the amount of traffic going to a Cisco ISE server
C. replaces all the other profiling probes
D. collects switch CPU and RAM usage for monitoring purposes
E. uses RADIUS authentication messages to send gathered data to a Cisco ISE server
Answer: D,E

NEW QUESTION: 4
You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training.data dataset Which property should you set?
A)

B)

C)

D)

A. Option A
B. Option B
C. Option D
D. Option C
Answer: A
Explanation:
Explanation
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder,
inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model


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