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• Step 1: Go to the Google Official Site
• Step 2: Read the instruction carefully
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• Step 4: Apply for the Professional Machine Learning Engineer Exam

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Google Professional Machine Learning Engineer Sample Questions (Q123-Q128):

NEW QUESTION # 123
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

• A. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
• B. Load the data into Cloud Bigtable, and read the data from Bigtable
• C. Load the data into BigQuery and read the data from BigQuery.
• D. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

Answer: B
NEW QUESTION # 124
You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator; estimator = tf.estimator.DNNRegressor( featurecolumns=[YOURLISTOFFEATURES], hidden_units-[1024, 512, 256], dropout=None) Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

• A. Switch from CPU to GPU serving
• B. Increase the dropout rate to 0.8 and retrain your model.
• C. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
• D. Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters

Answer: C Explanation:
Applying quantization to your SavedModel by reducing the floating point precision can help reduce the serving latency by decreasing the amount of memory and computation required to make a prediction. TensorFlow provides tools such as the tf.quantization module that can be used to quantize models and reduce their precision, which can significantly reduce serving latency without a significant decrease in model performance.
NEW QUESTION # 125
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

• A. Convolutional Neural Networks (CNN)
• B. Recurrent Neural Networks (RNN)
• C. Recommender system
• D. Reinforcement learning

Answer: A Explanation:
Convolutional Neural Networks (CNNs) are well-suited to image classification tasks such as identifying defects in products based on images. CNNs use convolutional layers that effectively extract features from images and can be trained to identify patterns in the images. The architecture of CNNs is optimized for image processing tasks and can be more efficient for extracting features from images than other types of neural networks.
Reinforcement learning is a type of machine learning that is used for problems with a delayed reward, such as game playing or robotics. Recommender system is used for recommending products or content to users based on their preferences. Recurrent Neural Networks (RNNs) are used for sequential data such as time series or natural language processing.
NEW QUESTION # 126
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

• A. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
• B. An optimization objective that maximizes the Precision at a Recall value of 0.50
• C. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
• D. An optimization objective that minimizes Log loss

Answer: A
NEW QUESTION # 127
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

• A. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
• B. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
• C. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
• D. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.

Answer: A
NEW QUESTION # 128
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