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Google Professional Machine Learning Engineer certification is a valuable asset for professionals who are looking to advance their careers in the field of machine learning. Google Professional Machine Learning Engineer certification validates the candidate's expertise in designing, building, and deploying machine learning models using the Google Cloud Platform. If you are a data scientist, machine learning engineer, or software developer looking to enhance your skills in machine learning, then the Google Professional Machine Learning Engineer certification is definitely worth considering.

Google Professional Machine Learning Engineer certification is a highly respected and sought-after certification in the field of machine learning. Google Professional Machine Learning Engineer certification is designed to validate the skills and expertise of professionals who are responsible for designing, building, managing, and deploying machine learning models at scale using Google Cloud technologies. Google Professional Machine Learning Engineer certification exam covers a wide range of topics related to machine learning, and candidates must have a minimum of three years of experience in the field of machine learning to be eligible for the exam.

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Google Professional Machine Learning Engineer Sample Questions (Q95-Q100):

NEW QUESTION # 95
You are creating a social media app where pet owners can post images of their pets. You have one million user uploaded images with hashtags. You want to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images.
What should you do?

A. Retrieve image labels and dominant colors from the input images using the Vision API. Use these properties and the hashtags to make recommendations.
B. Download a pretrained convolutional neural network, and use the model to generate embeddings of the input images. Measure similarity between embeddings to make recommendations.
C. Download a pretrained convolutional neural network, and fine-tune the model to predict hashtags based on the input images. Use the predicted hashtags to make recommendations.
D. Use the provided hashtags to create a collaborative filtering algorithm to make recommendations.

Answer: B

Explanation:
The best option to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images is to download a pretrained convolutional neural network (CNN), and use the model to generate embeddings of the input images. Embeddings are low-dimensional representations of high-dimensional data that capture the essential features and semantics of the data. By using a pretrained CNN, you can leverage the knowledge learned from large-scale image datasets, such as ImageNet, and apply it to your own domain. A pretrained CNN can be used as a feature extractor, where the output of the last hidden layer (or any intermediate layer) is taken as the embedding vector for the input image. You can then measure the similarity between embeddings using a distance metric, such as cosine similarity or Euclidean distance, and recommend images that have the highest similarity scores to the user's uploaded image. Option A is incorrect because downloading a pretrained CNN and fine-tuning the model to predict hashtags based on the input images may not capture the visual similarity of the images, as hashtags may not reflect the appearance of the images accurately. For example, two images of different breeds of dogs may have the same hashtag #dog, but they may not look similar to each other. Moreover, fine-tuning the model may require additional data and computational resources, and it may not generalize well to new images that have different or missing hashtags. Option B is incorrect because retrieving image labels and dominant colors from the input images using the Vision API may not capture the visual similarity of the images, as labels and colors may not reflect the fine-grained details of the images. For example, two images of the same breed of dog may have different labels and colors depending on the background, lighting, and angle of the image. Moreover, using the Vision API may incur additional costs and latency, and it may not be able to handle custom or domain-specific labels. Option C is incorrect because using the provided hashtags to create a collaborative filtering algorithm may not capture the visual similarity of the images, as collaborative filtering relies on the ratings or preferences of users, not the features of the images. For example, two images of different animals may have similar ratings or preferences from users, but they may not look similar to each other. Moreover, collaborative filtering may suffer from the cold start problem, where new images or users that have no ratings or preferences cannot be recommended. References:
* Image similarity search with TensorFlow
* Image embeddings documentation
* Pretrained models documentation
* Similarity metrics documentation

NEW QUESTION # 96
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?

A. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
B. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
C. Write a direct connection to the SQL database within the notebook and pull data in
D. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.

Answer: A

NEW QUESTION # 97
As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A. Use Cloud Functions for prediction each time a new data point is ingested
B. Deploy the model on Al Platform and create a version of it for online inference.
C. Create a serving pipeline in Compute Engine for prediction
D. Use the batch prediction functionality of Al Platform

Answer: D

Explanation:
Batch prediction is the process of using an ML model to make predictions on a large set of data points. Batch prediction is suitable for scenarios where the predictions are not time-sensitive and can be done in batches, such as digitizing scanned customer forms at the end of each day. Batch prediction can also handle large volumes of data and scale up or down the resources as needed. AI Platform provides a batch prediction service that allows users to submit a job with their TensorFlow model and input data stored in Cloud Storage, and receive the output predictions in Cloud Storageas well. This service requires minimal manual intervention and can be automated with Cloud Scheduler or Cloud Functions. Therefore, using the batch prediction functionality of AI Platform is the best option for this use case.
References:
* Batch prediction overview
* Using batch prediction

NEW QUESTION # 98
You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
B. data = json.dumps({"signature_name": "serving_default' "instances": [fab', 'be1, 'cd']]})
C. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b 'c'1, [d 'e T]]})
D. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c 'd'], ['e T]]})

Answer: C

NEW QUESTION # 99
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A. Increase the size of the training batch
B. Decrease the learning rate hyperparameter
C. Decrease the size of the training batch
D. Increase the learning rate hyperparameter

Answer: B

Explanation:
https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises

NEW QUESTION # 100
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