CertsTopics Logo

Big Cyber Monday Sale 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: save70

Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Machine-Learning-Engineer
Exam Name:
Google Professional Machine Learning Engineer
Certification:
Machine Learning Engineer
Vendor:
Google
Questions:
285
Last Updated:
Dec 4, 2025
Exam Status:
Stable
Google Professional-Machine-Learning-Engineer

Professional-Machine-Learning-Engineer: Machine Learning Engineer Exam 2025 Study Guide Pdf and Test Engine

Are you worried about passing the Google Professional-Machine-Learning-Engineer (Google Professional Machine Learning Engineer) exam? Download the most recent Google Professional-Machine-Learning-Engineer braindumps with answers that are 100% real. After downloading the Google Professional-Machine-Learning-Engineer exam dumps training , you can receive 99 days of free updates, making this website one of the best options to save additional money. In order to help you prepare for the Google Professional-Machine-Learning-Engineer exam questions and verified answers by IT certified experts, CertsTopics has put together a complete collection of dumps questions and answers. To help you prepare and pass the Google Professional-Machine-Learning-Engineer exam on your first attempt, we have compiled actual exam questions and their answers. 

Our (Google Professional Machine Learning Engineer) Study Materials are designed to meet the needs of thousands of candidates globally. A free sample of the CompTIA Professional-Machine-Learning-Engineer test is available at CertsTopics. Before purchasing it, you can also see the Google Professional-Machine-Learning-Engineer practice exam demo.

Get Professional-Machine-Learning-Engineer Full Access

Related Google Exams

Google Professional Machine Learning Engineer Questions and Answers

Get Free Professional-Machine-Learning-Engineer Questions and Answers
Question 1

You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google-recommended best practices?

Options:

A.

Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.

B.

Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

C.

Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with synchronous recognition.

D.

Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

Buy Now
Question 2

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

Options:

A.

1 Upload the audio files to Cloud Storage

2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions

3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

B.

1 Upload the audio files to Cloud Storage

2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.

3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method

C.

1 Iterate over your local Tiles in Python

2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data

3. Call the speech: recognize API endpoint to generate transcriptions

4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

D.

1 Iterate over your local files in Python

2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data

3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions

4 Call the Natural Language API by using the analyzesenriment method

Question 3

You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

Options:

A.

Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.

B.

Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri

environment variable to read the images by using the tf. data. Dataset. Iist_flies function.

C.

Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

D.

Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.

Get Free Professional-Machine-Learning-Engineer Questions and Answers