In today's world, the Professional-Machine-Learning-Engineer Google Professional Machine Learning Engineer certification exam has become increasingly popular, providing professionals with the opportunity to upskill and stay competitive in the tech industry. At NewPassLeader, we understand the importance of obtaining the Google Professional-Machine-Learning-Engineer Certification in the Google sector, where technological advancements constantly evolving.

The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

• Professional Cloud Architect was the highest paying certification of 2020 and 2019
• More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
• 87% of Google Cloud certified individuals are more confident about their cloud skills

Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

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Google Professional Machine Learning Engineer Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

• A. Increase the buffer size for the shuffle option.
• B. Set the prefetch option equal to the training batch size
• C. Decrease the batch size argument in your transformation
• D. Use the interleave option for reading data
• E. Reduce the value of the repeat parameter

Answer: B,D

NEW QUESTION # 41
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the dat a. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

• A. Use BigQuery ML to run several regression models, and analyze their performance.
• B. Read the data from BigQuery using Dataproc, and run several models using SparkML.
• C. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
• D. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.

Answer: A

NEW QUESTION # 42
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?

• A. Use one-hot encoding on all categorical features.
• B. Oversample the fraudulent transaction 10 times.
• C. Write your data in TFRecords.
• D. Z-normalize all the numeric features.

Answer: B

NEW QUESTION # 43
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

• A. Increase the buffer size for the shuffle option.
• B. Set the prefetch option equal to the training batch size
• C. Decrease the batch size argument in your transformation
• D. Use the interleave option for reading data
• E. Reduce the value of the repeat parameter

Answer: B,C

Explanation:
https://towardsdatascience.com/overcoming-data-preprocessing-bottlenecks-with-tensorflow-data-service-nvidia-dali-and-other-d6321917f851

NEW QUESTION # 44
You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

• A. Convert the speech to text and build a model based on the words
• B. Extract sentiment directly from the voice recordings
• C. Convert the speech to text and extract sentiments based on the sentences
• D. Convert the speech to text and extract sentiment using syntactical analysis

Answer: C

NEW QUESTION # 45
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