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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Machine Learning Engineer Certification Exam |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Exam Price: | $200 USD (plus tax where applicable) |
| Exam Duration: | 120 minutes |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Professional Data Engineer |
| Exam Format: | Multiple select, Multiple choice |
| Passing Score: | Not officially published, approximately 70% |
| Real Exam Qty: | 50-60 |
| Available Languages: | Japanese, English |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Official Exam Guide Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online-proctored remote exam or onsite-proctored exam at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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Google Professional Machine Learning Engineer Certification Exam is a professional certification that tests the knowledge and skills of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is designed to evaluate the proficiency of candidates in various aspects of machine learning, including data processing, modeling, and deployment. Google Professional Machine Learning Engineer certification is offered by Google Cloud, a subsidiary of Google that provides cloud computing services to businesses and individuals.
NEW QUESTION # 310
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data.
How should you address the input differences in production?
Answer: A
Explanation:
Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes.
https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew
NEW QUESTION # 311
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
Answer: B,C
Explanation:
Reference:
https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning#early-stopping
NEW QUESTION # 312
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?
Answer: A
Explanation:
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html
NEW QUESTION # 313
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
Answer: B
Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2 To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3 The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45
NEW QUESTION # 314
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow.
Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?
Answer: C
Explanation:
* Option A is incorrect because distributing the dataset with
tf.distribute.Strategy.experimental_distribute_dataset is not the most effective way to decrease the
* training time. This method allows you to distribute your dataset across multiple devices or machines, by creating a tf.data.Dataset instance that can be iterated over in parallel1. However, this option may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process. Moreover, this option may introduce additional overhead or complexity, as it requires you to handle the data sharding, replication, and synchronization across the devices or machines1.
* Option B is incorrect because creating a custom training loop is not the easiest way to decrease the training time. A custom training loop is a way to implement your own logic fortraining your model, by using low-level TensorFlow APIs, such as tf.GradientTape, tf.Variable, or tf.function2. A custom training loop may give you more flexibility and control over the training process, but it also requires more effort and expertise, as you have to write and debug the code for each step of the training loop, such as computing the gradients, applying the optimizer, or updating the metrics2. Moreover, a custom training loop may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process.
* Option C is incorrect because using a TPU with tf.distribute.TPUStrategy is not a valid way to decrease the training time. A TPU (Tensor Processing Unit) is a custom hardware acceleratordesigned for high-performance ML workloads3. A tf.distribute.TPUStrategy is a distribution strategy that allows you to distribute your training across multiple TPUs, by creating a tf.distribute.TPUStrategy instance that can be used with high-level TensorFlow APIs, such as Keras4. However, this option is not feasible, as Vertex AI Training does not support TPUs as accelerators for custom training jobs5. Moreover, this option may require significant code changes, as TPUs have different requirements and limitations than GPUs.
* Option D is correct because increasing the batch size is the best way to decrease the training time. The batch size is a hyperparameter that determines how many samples of data are processed in each iteration of the training loop. Increasing the batch size may reduce the training time, as it reduces the number of iterations needed to train the model, and it allows each device or machine to process more data in parallel. Increasing the batch size is also easy to implement, as it only requires changing a single hyperparameter. However, increasing the batch size may also affect the convergence and the accuracy of the model, so it is important to find the optimal batch size that balances the trade-off between the training time and the model performance.
References:
* tf.distribute.Strategy.experimental_distribute_dataset
* Custom training loop
* TPU overview
* tf.distribute.TPUStrategy
* Vertex AI Training accelerators
* [TPU programming model]
* [Batch size and learning rate]
* [Keras overview]
* [tf.distribute.MirroredStrategy]
* [Vertex AI Training overview]
* [TensorFlow overview]
NEW QUESTION # 315
......
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