Get May-2023 updated Exam Professional-Machine-Learning-Engineer Dumps with New Questions
100% Pass Guarantee for Professional-Machine-Learning-Engineer Exam Dumps with Actual Exam Questions
The Google Professional Machine Learning Engineer certification is highly valued in the industry and is recognized as a benchmark of excellence in the field of machine learning. Achieving this certification demonstrates that an individual has the skills and knowledge required to design and implement machine learning solutions at scale using Google Cloud technologies. The certification can help individuals advance their careers and open up new opportunities in the field of machine learning.
The Google Professional Machine Learning Engineer certification is highly valued in the industry and can lead to excellent career opportunities for individuals with expertise in this field. This certification is a testament to a candidate's ability to design, develop, and deploy machine learning models, and it can be a valuable asset for anyone seeking a career in machine learning or data science. Additionally, the certification demonstrates a candidate's knowledge of Google Cloud technologies and their ability to use them effectively to solve real-world problems.
The Google Professional Machine Learning Engineer Exam is a certification exam designed to validate an individual's expertise in machine learning engineering. This exam aims to assess the candidate's ability to create and deploy highly scalable, robust, and maintainable machine learning models using Google Cloud Platform technologies. The exam also tests the candidate's proficiency in designing and implementing machine learning architectures, solving business problems using machine learning, and optimizing machine learning workflows.
NEW QUESTION # 38
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?
- A. RMSE
- B. F1 score
- C. F-score where precision is weighed more than recall
- D. F-score where recall is weighed more than precision
Answer: D
NEW QUESTION # 39
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A. Execute the built-in FindDuplicates Amazon Athena query.
- B. Search for duplicate accounts in the AWS Glue Data Catalog.
- C. Create an AWS Glue crawler to infer duplicate accounts in the source data.
- D. Create a FindMatches machine learning transform in AWS Glue.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION # 40
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?
- A. Change preprocessing to use n-grams.
- B. Choose a different weight initialization type.
- C. Adjust hyperparameters related to the attention mechanism.
- D. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
Answer: D
NEW QUESTION # 41
You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure.
You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?
- A. The model with the highest precision where recall is greater than 0.5.
- B. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5
- C. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
- D. The model with the highest recall where precision is greater than 0.5.
Answer: D
Explanation:
In predictive maintenance, the goal is to identify which machines are likely to fail soon, so that the repair crew can fix them before they break. In this context, it is important to prioritize detection, while also ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure.
Recall is a metric that measures the proportion of actual positive observations that are correctly predicted as such by the model. In this case, recall is a good metric to use because it measures how well the model is able to identify the machines that are likely to fail soon.
Precision is a metric that measures the proportion of positive predictions that are actually true. In this case, precision is also important because it measures how many of the machines that the model predicts will fail soon, actually do fail soon.
By combining these two metrics, you can ensure that your model is able to identify the machines that are likely to fail soon with a high degree of accuracy. In this case, the model with the highest recall where precision is greater than 0.5 will be the best model, as it will have a high ability to identify the machines that are likely to fail soon and also it will have a high degree of accuracy.
Reference:
Recall and Precision
Predictive Maintenance
Metrics for classification
NEW QUESTION # 42
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
- A. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
- B. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
- C. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
- D. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
Answer: C
NEW QUESTION # 43
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. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- C. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- D. Create a tf.data.Dataset.prefetch transformation
Answer: A
Explanation:
Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset(). tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.
https://cloud.google.com/architecture/ml-on-gcp-best-practices#store-image-video-audio-and-unstructured-data-on-cloud-storage
" Store image, video, audio and unstructured data on Cloud Storage Store these data in large container formats on Cloud Storage. This applies to sharded TFRecord files if you're using TensorFlow, or Avro files if you're using any other framework. Combine many individual images, videos, or audio clips into large files, as this will improve your read and write throughput to Cloud Storage. Aim for files of at least 100mb, and between 100 and 10,000 shards. To enable data management, use Cloud Storage buckets and directories to group the shards. "
NEW QUESTION # 44
You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?
- A. Convert the categorical string data to one-hot hash buckets.
- B. Convert each categorical value into a run-length encoded string.
- C. Convert each categorical value into an integer value.
- D. Map the categorical variables into a vector of boolean values.
Answer: D
NEW QUESTION # 45
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?
- A. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
- B. Replace your test dataset with images of the newer products when they are introduced to retraining.
- C. Keep the original test dataset unchanged even if newer products are incorporated into retraining
- D. Extend your test dataset with images of the newer products when they are introduced to retraining
Answer: D
NEW QUESTION # 46
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. Use a TPU with tf.distribute.TPUStrategy.
- B. Increase the batch size.
- C. Create a custom training loop.
- D. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
Answer: A
NEW QUESTION # 47
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
- A. * Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue. - B. * Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - C. * Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - D. * Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction
Answer: B
Explanation:
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing
NEW QUESTION # 48
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?
- A. Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
- B. Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED. Give the report to the logistics team each month so they can fine-tune inventory levels.
- C. Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
- D. Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
Answer: A
NEW QUESTION # 49
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A. Misclassification rate
- B. Mean absolute percentage error (MAPE)
- C. Recall
- D. Area Under the ROC Curve (AUC)
Answer: D
NEW QUESTION # 50
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Use Al Platform Notebooks to run the classification model with pandas library
- B. Run a BigQuery ML task to perform logistic regression for the classification
- C. Configure AutoML Tables to perform the classification task
- D. Use Al Platform to run the classification model job configured for hyperparameter tuning
Answer: C
Explanation:
https://cloud.google.com/automl-tables/docs/beginners-guide
NEW QUESTION # 51
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- B. Address the model overfitting by using a less complex algorithm.
- C. Address data leakage by removing features highly correlated with the target value.
- D. Address data leakage by applying nested cross-validation during model training.
Answer: D
NEW QUESTION # 52
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- B. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- C. Import your user events and then your product catalog to make sure you have the highest quality event stream
- D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
Answer: B
Explanation:
Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.
NEW QUESTION # 53
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. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
- B. Build your custom container to run jobs on Al Platform Training
- C. Use a built-in model available on Al Platform Training
- D. Build your custom containers to run distributed training jobs on Al Platform Training
Answer: D
NEW QUESTION # 54
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?
- A. Increase the dropout rate to 0.8 and retrain your model.
- B. Switch from CPU to GPU serving
- 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 # 55
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 batch size
- B. Reduce the image shape
- C. Change the optimizer
- D. Change the learning rate
Answer: A
Explanation:
Reference:
https://stackoverflow.com/questions/59394947/how-to-fix-resourceexhaustederror-oom-when-allocating-tensor/59395251#:~:text=OOM%20stands%20for%20%22out%20of,in%20your%20Dense%20%2C%20Conv2D%20layers
NEW QUESTION # 56
A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?
- A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
- B. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
- C. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.
- D. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.
Answer: D
NEW QUESTION # 57
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?
- A. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
- B. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
- C. Train a model using AutoML Vision and use the "export for Coral" option.
- D. Train a model using AutoML Vision and use the "export for Core ML" option.
Answer: D
NEW QUESTION # 58
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?
- A. Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.
- B. Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
- C. Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
- D. Import the TensorFlow model with BigQuery ML, and run the ml.predict function.
Answer: D
NEW QUESTION # 59
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 feature expectations are captured in the schema
- B. Ensure that training is reproducible
- C. Ensure that model performance is monitored
- D. Ensure that all hyperparameters are tuned
Answer: B
Explanation:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf
NEW QUESTION # 60
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?
- A. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
- B. Split the training and test data based on time rather than a random split to avoid leakage
- C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
- D. Normalize the data for the training, and test datasets as two separate steps.
Answer: B
Explanation:
https://community.rapidminer.com/discussion/32592/normalising-data-before-data-split-or-after
NEW QUESTION # 61
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?
- A. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR
- B. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR
- C. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- D. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
Answer: C
Explanation:
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/
NEW QUESTION # 62
While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?
- A. Add caching to the pipeline
- B. Add parallel interleave to the pipeline
- C. Increase the CPU load
- D. Increase the network bandwidth
Answer: C
NEW QUESTION # 63
......
Professional-Machine-Learning-Engineer exam dumps with real Google questions and answers: https://www.trainingquiz.com/Professional-Machine-Learning-Engineer-practice-quiz.html
Today Updated Professional-Machine-Learning-Engineer Exam Dumps Actual Questions: https://drive.google.com/open?id=1xt5OnhqGjZn0kGfEiNA0q0Z6-ty4eBsu
