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Lead2PassExam Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) practice test material covers all the key topics and areas of knowledge necessary to master the Google Certification Exam. Experienced industry professionals design the Professional-Machine-Learning-Engineer exam questions and are regularly updated to reflect the latest changes in the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) exam. In addition, Lead2PassExam offers three different formats of practice material which are discussed below.
| Section | Objectives |
|---|---|
| Data preparation and processing | - Data ingestion and pipelines
|
| ML model development | - Model training and tuning
|
| ML pipeline automation and orchestration | - Pipeline design
|
| Designing ML solutions | - ML architecture design
|
| Deployment and operations | - Monitoring and maintenance
|
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The study material is available in three formats, i.e. PDF format, web-based practice exam, and desktop practice test software. The PDF format is easy for those who always have their smart devices and love to study from them. Users can also make notes of printed PDF Google Google Professional Machine Learning Engineer certification exam so they can study them anywhere to pass Google Professional-Machine-Learning-Engineer Certification test with a good score.
NEW QUESTION # 174
You need to deploy an image classification model to 4,000 handheld barcode scanners that have
512 MB of RAM, an ARM CPU, and intermittent connectivity. Your trained model is a 340 MB float32 SavedModel with inference latency of 900 ms on the target hardware. Accuracy may decrease by up to two percentage points. What should you do?
Answer: A
Explanation:
INT8 post-training quantization reduces model size roughly fourfold and speeds up integer arithmetic on ARM CPUs, while TensorFlow Lite provides the on-device runtime. Validating the quantized model against the held-out set confirms the accuracy loss remains within the stated two-point tolerance, and on-device inference tolerates intermittent connectivity.
NEW QUESTION # 175
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( feature_columns=[YOUR_LIST_OF_FEATURES], 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?
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 # 176
You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost.
What should you do?
Answer: C
Explanation:
https://cloud.google.com/blog/topics/developers-practitioners/monitor-models-training-serving-skew-vertex-ai
https://developers.google.com/machine-learning/guides/rules-of-ml
NEW QUESTION # 177
You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?
Answer: C
NEW QUESTION # 178
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy.
Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
Answer: C
Explanation:
The best option for performing hyperparameter tuning on Vertex AI to determine the appropriate embedding dimension and the optimal learning rate is to use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials. This option has the following advantages:
* It matches the appropriate scaling type for each hyperparameter, based on their range and distribution.
The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LINEAR_SCALE makes sense. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LOG_SCALE is more suitable.
* It maximizes the exploration of the hyperparameter space, by using a large number of parallel trials.
Since training time is not a concern, using more trials can help find the best combination of hyperparameters that maximizes model accuracy. The default Bayesian optimization tuning algorithm can efficiently sample the hyperparameter space and converge to the optimal values.
The other options are less optimal for the following reasons:
* Option B: Using UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials, reduces the exploration of the hyperparameter space, by using a small number of parallel trials. Since training time is not a concern, using fewer trials can miss some potentially good combinations of hyperparameters that maximize model accuracy. The default Bayesian optimization tuning algorithm can benefit from more trials to sample the hyperparameter space and converge to the optimal values.
* Option C: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials, mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LOG_SCALE is not suitable.
The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LINEAR_SCALE makes less sense.
* Option D: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials, combines the drawbacks of option B and option C. It mismatches the appropriate scaling type for each hyperparameter, based ontheir range and distribution, and reduces the exploration of the hyperparameter space, by using a small number of parallel trials.
References:
* [Vertex AI: Hyperparameter tuning overview]
* [Vertex AI: Configuring the hyperparameter tuning job]
NEW QUESTION # 179
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