Professional-Machine-Learning-Engineer Exam Study Guide Free Practice Test LAST UPDATED DATE Feb 28, 2024
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Career Bonuses
The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.
NEW QUESTION # 65
You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps
1. Randomly split the data into training and evaluation datasets in a 65/35 ratio
2. Conduct feature engineering
3 Obtain metrics for the evaluation dataset.
4 Compare models trained in different pipeline executions
How should you execute these steps'?
- A. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2. Enable auto logging of metrics in the training component.
3 Compare pipeline runs in Vertex Al Experiments - B. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2 Enable autologging of metrics in the training component
3 Compare models using the artifacts lineage in Vertex ML Metadata - C. 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits.
2 Use a SQL view to apply feature engineering and train the model using the data in that view
3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement. - D. 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits.
2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table
Answer: A
Explanation:
' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.
NEW QUESTION # 66
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
- B. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
- C. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- D. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
Answer: B
NEW QUESTION # 67
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
- A. Use an established text classification model on Al Platform as-is to classify support requests
- B. Use AutoML Natural Language to build the support requests classifier
- C. Use the Natural Language API to classify support requests
- D. Use an established text classification model on Al Platform to perform transfer learning
Answer: A
NEW QUESTION # 68
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
- A. Normalize the data using Google Kubernetes Engine
- B. Translate the normalization algorithm into SQL for use with BigQuery
- C. Use the normalizer_fn argument in TensorFlow's Feature Column API
- D. Normalize the data with Apache Spark using the Dataproc connector for BigQuery
Answer: B
NEW QUESTION # 69
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers
- A. Set the prefetch option equal to the training batch size
- B. Reduce the value of the repeat parameter
- C. Increase the buffer size for the shuffle option.
- D. Decrease the batch size argument in your transformation
- E. Use the interleave option for reading data
Answer: D,E
NEW QUESTION # 70
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
- A. Linear regression
- B. Principal component analysis (PCA)
- C. Logistic regression
- D. K-means
Answer: A
NEW QUESTION # 71
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?
- A. Run self-correlation on all features and remove highly correlated features
- B. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
- C. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
- D. Normalize all numerical values to be between 0 and 1
Answer: D
NEW QUESTION # 72
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A. Recall
- B. Mean absolute percentage error (MAPE)
- C. Area Under the ROC Curve (AUC)
- D. Misclassification rate
Answer: C
NEW QUESTION # 73
You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?
- A. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.
- B. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
- C. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
- D. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
Answer: B
NEW QUESTION # 74
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
- A. Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
- B. Create a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.
- C. Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
- D. Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.
Answer: A
NEW QUESTION # 75
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. Add a regularization term such as the Min-Diff algorithm to the loss function.
- C. Remove moderation for languages for which the false positive rate is too high.
- D. Replace the in-house word2vec with GPT-3 or T5.
Answer: C
NEW QUESTION # 76
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 to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- B. Create a tf.data.Dataset.prefetch transformation
- C. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- D. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
Answer: D
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 # 77
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
- A. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
- B. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
- C. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
- D. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
Answer: D
NEW QUESTION # 78
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?
- A. There is training-serving skew in your production environment.
- B. There is not a sufficient amount of training data.
- C. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
- D. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
Answer: A
Explanation:
This is the most likely problem that is occurring based on the information provided. Training-serving skew occurs when the distribution of the data used for training and the data used for serving the model in production are different. This can result in a drop in model performance when the model is deployed to production. It's also possible that the model is overfitting during training.
It is not a problem of insufficient amount of data because the data is split by using the BigQuery and it's not a problem of sharing some records between tables because it is not mentioned that the data is shared in the question.
The problem D is also not correct as the RAND() function is used to split the data but it doesn't mean that every record in the validation table will also be in the training table.
NEW QUESTION # 79
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
- A. Traceability, reproducibility, and explainability
- B. Redaction, reproducibility, and explainability
- C. Differential privacy federated learning, and explainability
- D. Federated learning, reproducibility, and explainability
Answer: A
NEW QUESTION # 80
You work for an online travel agency that also sells advertising placements on its website to other companies.
You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?
- A. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
- B. Embed the client on the website, and then deploy the model on AI Platform Prediction.
- C. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
- D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.
Answer: D
Explanation:
https://medium.com/google-cloud/secure-cloud-run-cloud-functions-and-app-engine-with-api-key-73c57bededd1
NEW QUESTION # 81
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
- B. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- D. Significantly increase the max_batch_size TensorFlow Serving parameter
Answer: B
NEW QUESTION # 82
You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?
- A. Load the data into a pandas DataFrame Implement the preprocessing steps using panda's transformations. and train the model directly on the DataFrame.
- B. Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow Save the preprocessed data as CSV files in a Cloud Storage bucket.
- C. Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc Save the preprocessed data as CSV files in a Cloud Storage bucket.
- D. Perform preprocessing in BigQuery by using SQL Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.
Answer: B
NEW QUESTION # 83
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Google Professional Machine Learning Engineer Certification Exam is a certification program designed to validate the skills and knowledge of professionals in the field of machine learning engineering. Google Professional Machine Learning Engineer certification is offered by Google Cloud and is intended for individuals who have experience in building and deploying machine learning models using Google Cloud technologies. Google Professional Machine Learning Engineer certification exam assesses the candidate's ability to design, build, and operate machine learning solutions that meet business objectives.
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