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Prepare Professional-Machine-Learning-Engineer Exam Questions [2023] Recently Updated Questions
Google Professional Machine Learning Engineer certification exam is a rigorous and comprehensive test that measures the knowledge and skills of individuals who want to become certified machine learning engineers. Developed by Google, this certification exam is designed to assess the candidate's ability to design, develop, deploy, and maintain machine learning models that can solve business problems.
NEW QUESTION # 39
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?
- A. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
- B. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
- C. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
- D. Create a hot-encoding of words, and feed the encodings into your model.
Answer: A
NEW QUESTION # 40
You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?
- A. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
- B. Load the model directly into the Dataflow job as a dependency, and use it for prediction.
- C. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
- D. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.
Answer: C
NEW QUESTION # 41
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 area under the receiver operating characteristic curve (AUC ROC) value
- B. An optimization objective that maximizes the Precision at a Recall value of 0.50
- 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
NEW QUESTION # 42
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. Misclassification rate
- C. Mean absolute percentage error (MAPE)
- D. Area Under the ROC Curve (AUC)
Answer: D
NEW QUESTION # 43
You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?
- A. Hire a team of qualified analysts to review and label the machines' historical performance data. Train a model based on this manually labeled dataset.
- B. Develop a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Test this heuristic in a production environment.
- C. Train a time-series model to predict the machines' performance values. Configure an alert if a machine's actual performance values significantly differ from the predicted performance values.
- D. Implement a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Train a model to predict anomalies based on this labeled dataset.
Answer: A
NEW QUESTION # 44
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?
- A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
- B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
Answer: B
NEW QUESTION # 45
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?
- A. A histogram showing whether the most important input feature is Gaussian.
- B. A scatter plot showing the performance of the objective metric over each training iteration.
- C. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
- D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: C
NEW QUESTION # 46
You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?
- A. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.
- B. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
- C. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
- D. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
Answer: D
NEW QUESTION # 47
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 Dataset. from_tensor_slices{).
- B. Create a tf.data.Dataset.prefetch transformation
- 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 tf. data. Dataset. from_tensors ().
Answer: C
NEW QUESTION # 48
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
- A. Uniform distribution
- B. Normal distribution
- C. Binomial distribution
- D. Poisson distribution
Answer: C
NEW QUESTION # 49
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
- A. Create a new view with BigQuery that does not include a column with city information
- B. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- C. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
- D. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
Answer: C
NEW QUESTION # 50
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Choose two.)
- A. The k-means algorithm
- B. The Latent Dirichlet Allocation (LDA) algorithm
- C. The factorization machines (FM) algorithm
- D. The principal component analysis (PCA) algorithm
- E. The Random Cut Forest (RCF) algorithm
Answer: A,D
Explanation:
Explanation/Reference:
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION # 51
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
- A. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
- B. Use the Cloud Natural Language API to extract custom entities for classification
- C. Use the Al Platform Training built-in algorithms to create a custom model
- D. Use AutoML Natural Language to extract custom entities for classification
Answer: D
NEW QUESTION # 52
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?
- A. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
- B. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe
- C. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
- D. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
Answer: C
NEW QUESTION # 53
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. Normalize the data for the training, and test datasets as two separate steps.
- B. Split the training and test data based on time rather than a random split to avoid leakage
- C. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
- D. Add more data to your test set to ensure that you have a fair distribution and sample for testing
Answer: B
Explanation:
https://community.rapidminer.com/discussion/32592/normalising-data-before-data-split-or-after
NEW QUESTION # 54
A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)
- A. Modify the ACL on the endpoint network interface to restrict access to the instances.
- B. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
- C. Modify the security group on the endpoint network interface to restrict access to the instances.
- D. Add a VPC endpoint policy to allow access to the IAM users.
- E. Add a SageMaker Runtime VPC endpoint interface to the VPC.
Answer: C,D
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon- sagemaker-running-in-internet-free-mode/
NEW QUESTION # 55
You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?
- A. Reduce the number of layers in the model architecture
- B. Configure your model to use bfloat16 instead float32
- C. Reduce the dimensions of the images used un the model
- D. Reduce the global batch size from 1024 to 256
Answer: D
NEW QUESTION # 56
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To be eligible for the exam, candidates should have experience in machine learning, including designing and implementing machine learning models, as well as experience with cloud-based machine learning services. Candidates should also have experience with data engineering, data analysis, and software engineering. Professional-Machine-Learning-Engineer exam is intended for individuals who have at least three years of experience in the field, and who are able to demonstrate their knowledge through a combination of multiple choice and practical exam questions.
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