Authentic Google Professional-Machine-Learning-Engineer Exam Dumps PDF - 2024 Updated
Get Prepared for Your Professional-Machine-Learning-Engineer Exam With Actual 267 Questions
What is the duration, language, and format of Professional Machine Learning Engineer - Google
- No negative marking for wrong answers
- Duration of Exam: 120 minutes
- Language of Exam: English, Japanese, Korean
- Type of Questions: Multiple choice (MCQs), multiple answers
Google Professional Machine Learning Engineer Certification Exam is considered to be one of the most challenging and comprehensive exams in the field of machine learning. Candidates who pass the exam are recognized as experts in their field and are highly sought-after by employers worldwide. Earning this certification can open up a wide range of career opportunities, including roles such as machine learning engineer, data scientist, and artificial intelligence specialist. Moreover, it also demonstrates a candidate's commitment to ongoing learning and professional development, which is highly valued by employers in today's rapidly evolving technology landscape.
Google Professional Machine Learning Engineer Certification Exam is a test designed to validate advanced skills in machine learning. Professional-Machine-Learning-Engineer exam is intended for professionals who want to demonstrate their ability to design and develop machine learning models on the Google Cloud platform. Google Professional Machine Learning Engineer certification exam is a great way for professionals to showcase their expertise in developing and deploying machine learning models.
NEW QUESTION # 137
You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?
- A. Import the model into Vertex Al On Vertex Al Pipelines, create a pipeline that uses the DatafIowPythonJobOp and the ModelBatchPredictOp components.
- B. Import the model into BigQuery Implement the data processing logic in a SQL query On Vertex Al Pipelines create a pipeline that uses the BigqueryQueryJobop and the EigqueryPredictModejobOp components.
- C. Import the model into Vertex Al and deploy it to a Vertex Al endpoint On Vertex Al Pipelines create a pipeline that uses the Dataf lowPythonJobop and the Mcdei3archPredictoc components.
- D. Import the model into Vertex Al and deploy it to a Vertex Al endpoint Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint and then uploads predictions to a BigQuery table.
Answer: C
NEW QUESTION # 138
You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?
- A. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
- B. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
- C. Use TensorFlow I/O's BigQuery Reader to directly read the data.
- D. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.
Answer: C
Explanation:
TensorFlow I/O's BigQuery Reader allows you to directly read data from BigQuery tables into your TensorFlow model without the need to export the data to a separate file format. This can minimize any bottlenecks during the data ingestion stage and also it can increase the scalability. By using BigQuery Reader, you can easily read large amounts of data from BigQuery and use it to train your model without having to worry about the performance impact of reading from a dataframe or CSV file.
You can use the tfio.BigQueryRecordDataset which will return a dataset of dictionaries, and where each key corresponds to a table column and each value corresponds to the value in that column.
NEW QUESTION # 139
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?
- A. Model distillation
- B. Dynamic range quantization
- C. Weight pruning
- D. Dimensionality reduction
Answer: B
Explanation:
Dynamic range quantization is a model optimization technique for reducing latency that reduces the numerical precision of the weights and activations of models. This technique can reduce the model size, memory usage, and inference time by up to 4x with negligible accuracy loss. Dynamic range quantization can be applied to a trained TensorFlow model without retraining, and it is suitable for mobile applications that require low latency and power consumption.
Weight pruning, model distillation, and dimensionality reduction are also model optimization techniques for reducing latency, but they have some limitations or drawbacks compared to dynamic range quantization:
* Weight pruning works by removing parameters within a model that have only a minor impact on its predictions. Pruned models are the same size on disk, and have the same runtime latency, but can be compressed more effectively. This makes pruning a useful technique for reducing model download size, but not for reducing inference time.
* Model distillation works by training a smaller and simpler model (student) to mimic the behavior of a larger and complex model (teacher). Distilled models can have lower latency and memory usage than the original models, but they require retraining and may not preserve the accuracy of the teacher model.
* Dimensionality reduction works by reducing the number of features or dimensions in the input data or the model layers. Dimensionality reduction can improve the computational efficiency and generalization ability of models, but it may also lose some information or introduce noise in the data or the model.
Dimensionality reduction also requires retraining or modifying the model architecture.
References:
* [TensorFlow Model Optimization]
* [TensorFlow Model Optimization Toolkit - Post-Training Integer Quantization]
* [Model optimization methods to cut latency, adapt to new data]
NEW QUESTION # 140
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- A. Create a tf.data.Dataset.prefetch transformation
- B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- C. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- D. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
Answer: D
NEW QUESTION # 141
You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?
- A. 1 Use BigQuery to scale the numerical features.
2. Feed the features into Vertex Al Training.
3 Allow TensorFlow to perform the one-hot text encoding. - B. 1 Write a SQL query to create a separate lookup table to scale the numerical features.
2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.
3. Feed the resulting BigQuery view into Vertex Al Training. - C. 1 Write a SQL query to create a separate lookup table to scale the numerical features.
2 Perform the one-hot text encoding in BigQuery.
3. Feed the resulting BigQuery view into Vertex Al Training. - D. 1 Use TFX components with Dataflow to encode the text features and scale the numerical features.
2 Export results to Cloud Storage as TFRecords.
3 Feed the data into Vertex Al Training.
Answer: D
Explanation:
TFX (TensorFlow Extended) is a platform for end-to-end machine learning pipelines. It provides components for data ingestion, preprocessing, validation, model training, serving, and monitoring. Dataflow is a fully managed service for scalable data processing. By using TFX components with Dataflow, you can perform feature engineering on large-scale tabular data in a distributed and efficient way. You can use the Transform component to apply the MaxMin scaler and the one-hot encoding to the numerical and categorical features, respectively. You can also use the ExampleGen component to read data from BigQuery and the Trainer component to train your TensorFlow model. The output of the Transform component is a TFRecord file, which is a binary format for storing TensorFlow data. You can export the TFRecord file to Cloud Storage and feed it into Vertex AI Training, which is a managed service for training custom machine learning models on Google Cloud. References:
* TFX | TensorFlow
* Dataflow | Google Cloud
* Vertex AI Training | Google Cloud
NEW QUESTION # 142
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that all hyperparameters are tuned
- B. Ensure that feature expectations are captured in the schema
- C. Ensure that model performance is monitored
- D. Ensure that training is reproducible
Answer: C
Explanation:
* Monitoring model performance is an essential part of production readiness, as it allows the team to detect and address any issues that may arise after deployment, such as data drift, model degradation, or errors.
* Other Options:
* A. Ensuring that training is reproducible is important for model development, but not necessarily for production readiness. Reproducibility helps the team to track and compare different experiments, but it does not guarantee that the model will perform well in production.
* B. Ensuring that all hyperparameters are tuned is also important for model development, but not sufficient for production readiness. Hyperparameter tuning helps theteam to find the optimal configuration for the model, but it does not account for the dynamic and changing nature of the production environment.
* D. Ensuring that feature expectations are captured in the schema is a part of testing features and data, which the team has already done. The schema defines the expected format, type, and range of the features, and helps the team to validate and preprocess the data.
NEW QUESTION # 143
You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?
- A. Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.
- B. Train an object detection model in Vertex Al custom training by using the annotated image data.
- C. Train an object detection model in AutoML by using the annotated image data.
- D. Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.
Answer: C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". AutoML Vision2 is a service that allows you to train and deploy custom vision models for image classification and object detection. AutoML Vision simplifies the model development process by providing a graphical user interface and a no-code approach. You can use AutoML Vision to train an object detection model by using the annotated image data, and evaluate the model performance using metrics such as mean average precision (mAP) and intersection over union (IoU)3. Therefore, option B is the best way to quickly create an initial model for the given use case. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* AutoML Vision
* Object detection evaluation
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 144
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 Precision at a Recall value of 0.50
- B. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
- C. An optimization objective that minimizes Log loss
- D. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
Answer: D
Explanation:
https://stats.stackexchange.com/questions/262616/roc-vs-precision-recall-curves-on-imbalanced-dataset
https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc
https://icaiit.org/proceedings/6th_ICAIIT/1_3Fayzrakhmanov.pdf The problem of fraudulent transactions detection, which is an imbalanced classification problem (most transactions are not fraudulent), you want to maximize both precision and recall; so the area under the PR curve. As a matter of fact, the question asks you to focus on detecting fraudulent transactions (maximize true positive rate, a.k.a. Recall) while minimizing false positives (a.k.a. maximizing Precision). Another way to see it is this: for imbalanced problems like this one you'll get a lot of true negatives even from a bad model (it's easy to guess a transaction as "non-fraudulent" because most of them are!), and with high TN the ROC curve goes high fast, which would be misleading. So you wanna avoid dealing with true negatives in your evaluation, which is precisely what the PR curve allows you to do.
NEW QUESTION # 145
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?
- A. Build your custom container to run jobs on Al Platform Training
- B. Build your custom containers to run distributed training jobs on Al Platform Training
- C. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
- D. Use a built-in model available on Al Platform Training
Answer: B
Explanation:
"ML framework and related dependencies are not supported by Al Platform Training" use custom containers "your model and your data are too large to fit in memory on a single machine " use distributed learning techniques
NEW QUESTION # 146
You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do?
Choose 2 answers
- A. Collect a stratified sample of production traffic to build the training dataset.
- B. Include a comprehensive set of demographic features.
- C. Collect a random sample of production traffic to build the training dataset.
- D. Conduct fairness tests across sensitive categories and demographics on the trained model.
- E. include only the demographic groups that most frequently interact with advertisements.
Answer: A,C
NEW QUESTION # 147
You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE.
What should you do with the data before it is made available to the data science team for training purposes?
- A. Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible.
- B. Tokenize all of the fields using hashed dummy values to replace the real values.
- C. Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.
- D. Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.
Answer: A
Explanation:
The best option for protecting sensitive customer data that might be used in the ML models is to coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGITUDE into single precision. This option has the following advantages:
* It preserves the utility and relevance of the data for the ML models, as the coarsened data still captures the essential information and patterns that the models need to learn. For example, putting AGE into quantiles can group the customers into different age ranges, which can be useful for predicting their preferences or behavior. Rounding LATITUDE_LONGITUDE into single precision can reduce the precision of the location data, but still retain the general geographic region of the customers, which can be useful for personalizing the recommendations or offers.
* It reduces the risk of exposing the personal or private information of the customers, as the coarsened data makes it harder to identify or re-identify the individual customers from the data. For example, putting AGE into quantiles can hide the exact age of the customers, which can be considered sensitive or confidential. Rounding LATITUDE_LONGITUDE into single precision can obscure the exact location of the customers, which can be considered sensitive or confidential.
The other options are less optimal for the following reasons:
* Option A: Tokenizing all of the fields using hashed dummy values to replace the real values eliminates the utility and relevance of the data for the ML models, as the tokenized data loses all the information and patterns that the models need to learn. For example, tokenizing AGE using hashed dummy values can make the data meaningless and irrelevant, as the models cannot learn anything from the random tokens. Tokenizing LATITUDE_LONGITUDE using hashed dummy values can make the data meaningless and irrelevant, as the models cannot learn anything from the random tokens.
* Option B: Using principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector reduces the utility and relevance of the data for the ML models, as the PCA vector may not capture all the information and patterns that the models need to learn. For example, using PCA to reduce AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE to one PCA vector can lose some information or introduce noise in the data, as the PCA vector is a linear combination of the original features, which may not reflect their true relationship or importance. Moreover, using PCA to reduce the four sensitive fields to one PCA vector may not reduce the risk of exposing the personal or private information of the customers,as the PCA vector may still be reversible or linkable to the original data, depending on the amount of variance explained by the PCA vector and the availability of the PCA transformation matrix.
* Option D: Removing all sensitive data fields, and asking the data science team to build their models using non-sensitive data reduces the utility and relevance of the data for the ML models, as the non-sensitive data may not contain enough information and patterns that the models need to learn. For example, removing AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE from the data can make the data insufficient and unrepresentative, as the models may not be able to learn the factors that influence the customers' preferences or behavior. Moreover, removing all sensitive data fields from the data may not be necessary or feasible, as the data protection legislation may allow the use of sensitive data for the ML models, as long as the data is processed in a secure and ethical manner, and the customers' consent and rights are respected.
References:
* Protecting Sensitive Data and AI Models with Confidential Computing | NVIDIA Technical Blog
* Training machine learning models from sensitive data | Fast Data Science
* Securing ML applications. Model security and protection - Medium
* Security of AI/ML systems, ML model security | Cossack Labs
* Vulnerabilities, security and privacy for machine learning models
NEW QUESTION # 148
You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
- A. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
- B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
- C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
- D. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
Answer: A
Explanation:
* Dataflow is a fully managed service for executing Apache Beam pipelines that can process streaming or batch data1.
* Al Platform is a unified platform that enables you to build and run machine learning applications across Google Cloud2.
* BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility3.
These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization. The other options are not as suitable because:
* DataProc is a service for running Apache Spark and Apache Hadoop clusters, which are not optimized
* for streaming data processing4.
* AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs5. However, it does not support custom models or real-time predictions.
* Cloud Bigtable is a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis.
* Cloud Functions is a serverless execution environment for building and connecting cloud services.
However, it is not suitable for storing or visualizing data.
* Cloud Storage is a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.
NEW QUESTION # 149
You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?
- A. Use TensorFlow I/O's BigQuery Reader to directly read the data.
- B. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
- C. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
- D. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.
Answer: B
NEW QUESTION # 150
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
- A. Three individual features binned latitude, binned longitude, and one-hot encoded car type
- B. One feature obtained as an element-wise product between latitude, longitude, and car type
- C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
- D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
Answer: C
Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
NEW QUESTION # 151
You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?
- A. Containerize the PySpark transformation step, and add it to your pipeline.
- B. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
- C. Remove the data transformation step from your pipeline.
- D. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
Answer: D
NEW QUESTION # 152
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?
- A. Increase the batch size.
- B. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
- C. Create a custom training loop.
- D. Use a TPU with tf.distribute.TPUStrategy.
Answer: C
Explanation:
This would allow you to tailor the training process to your specific needs and requirements, and it would also allow for more flexible experimentation with different training strategies.
Additionally, creating a custom training loop could result in faster training times compared to using a single GPU or the distributed training strategies currently available in Keras.
NEW QUESTION # 153
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?
- A. Reduce the image shape
- B. Change the learning rate
- C. Reduce the batch size
- D. Change the optimizer
Answer: C
NEW QUESTION # 154
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than
20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?
- A. Train a classifier using the chat messages in their original language.
- B. Replace the in-house word2vec with GPT-3 or T5.
- C. Remove moderation for languages for which the false positive rate is too high.
- D. Add a regularization term such as the Min-Diff algorithm to the loss function.
Answer: A
Explanation:
The problem with the current approach is that it relies on the Cloud Translation API to translate the chat messages into a common language before embedding them with the in-house word2vec model. This introduces two sources of error: the translation quality and the word2vec quality. The translation quality may vary across different languages, depending on the availability of data and the complexity of the grammar and vocabulary.
The word2vec quality may also vary depending on the size and diversity of the corpus used to train it. These errors may affect the performance of the classifier that moderates the chat messages, resulting in significant differences across the languages.
A better approach would be to train a classifier using the chat messages in their original language, without relying on the Cloud Translation API or the in-house word2vec model. This way, the classifier can learn the nuances and subtleties of each language, and avoid the errors introduced by the translation and embedding processes. This would also reduce the latency and cost of the moderation system, as it would not need to invoke the Cloud Translation API for every message. To train a classifier using the chat messages in their original language, one could use a multilingual pre-trained model such as mBERT or XLM-R, which can handle multiple languages and domains. Alternatively, one could train a separate classifier for each language, using a monolingual pre-trained model such as BERT or a custom model tailored to the specific language and task.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* [mBERT: Bidirectional Encoder Representations from Transformers]
* [XLM-R: Unsupervised Cross-lingual Representation Learning at Scale]
* [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]
NEW QUESTION # 155
......
Accurate & Verified New Professional-Machine-Learning-Engineer Answers As Experienced in the Actual Test!: https://gcgapremium.pass4leader.com/Google/Professional-Machine-Learning-Engineer-exam.html