1. Home
  2. Microsoft
  3. DP-100 Dumps

Eliminate Risk of Failure with Microsoft DP-100 Exam Dumps

Schedule your time wisely to provide yourself sufficient time each day to prepare for the Microsoft DP-100 exam. Make time each day to study in a quiet place, as you'll need to thoroughly cover the material for the Designing and Implementing a Data Science Solution on Azure exam. Our actual Azure Data Scientist Associate exam dumps help you in your preparation. Prepare for the Microsoft DP-100 exam with our DP-100 dumps every day if you want to succeed on your first try.

All Study Materials

Instant Downloads

24/7 costomer support

Satisfaction Guaranteed

Q1.

You have a dataset that includes confidential dat

a. You use the dataset to train a model.

You must use a differential privacy parameter to keep the data of individuals safe and private.

You need to reduce the effect of user data on aggregated results.

What should you do?

Answer: C

See the explanation below.

Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.


https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

Q2.

You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.

You develop a training script for the model on a local machine.

You need to load the model fairness metrics into Azure Machine Learning studio.

What should you do?

Answer: C

See the explanation below.

import azureml.contrib.fairness package to perform the upload:

from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

Q3.

You use the Azure Machine Learning designer to create and run a training pipeline.

The pipeline must be run every night to inference predictions from a large volume of files. The folder where the files will be stored is defined as a dataset.

You need to publish the pipeline as a REST service that can be used for the nightly inferencing run.

What should you do?

Answer: A

See the explanation below.

Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.

You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline.


https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md

Q4.

You train and register a machine learning model. You create a batch inference pipeline that uses the model to generate predictions from multiple data files.

You must publish the batch inference pipeline as a service that can be scheduled to run every night.

You need to select an appropriate compute target for the inference service.

Which compute target should you use?

Answer: B

See the explanation below.

Azure Machine Learning compute clusters is used for Batch inference. Run batch scoring on serverless compute. Supports normal and low-priority VMs. No support for real-time inference.


https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

Q5.

You have a Python script that executes a pipeline. The script includes the following code:

from azureml.core import Experiment

pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)

You want to test the pipeline before deploying the script.

You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

Which code segment should you add to the test script?

Answer: B

See the explanation below.

wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.

Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True)

Parameter: show_output

Indicates whether to show the run output on sys.stdout.


Are You Looking for More Updated and Actual Microsoft DP-100 Exam Questions?

If you want a more premium set of actual Microsoft DP-100 Exam Questions then you can get them at the most affordable price. Premium Azure Data Scientist Associate exam questions are based on the official syllabus of the Microsoft DP-100 exam. They also have a high probability of coming up in the actual Designing and Implementing a Data Science Solution on Azure exam.
You will also get free updates for 90 days with our premium Microsoft DP-100 exam. If there is a change in the syllabus of Microsoft DP-100 exam our subject matter experts always update it accordingly.