New 2024 Realistic Free Google Professional-Machine-Learning-Engineer Exam Dump Questions and Answer
Professional-Machine-Learning-Engineer Practice Test Engine: Try These 150 Exam Questions
NEW QUESTION # 24
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
- A. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
- B. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
- C. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
- D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
Answer: C
NEW QUESTION # 25
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
- A. Build the Docker container to be NVIDIA-Docker compatible.
- B. Bundle the NVIDIA drivers with the Docker image.
- C. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
- D. Organize the Docker container's file structure to execute on GPU instances.
Answer: B
NEW QUESTION # 26
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
- A. Compare the mean average precision across the models using the Continuous Evaluation feature
- B. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
- C. Compare the loss performance for each model on a held-out dataset.
- D. Compare the loss performance for each model on the validation data
Answer: D
NEW QUESTION # 27
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?
- A. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction - B. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction. - C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map - D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
Answer: C
Explanation:
This is a case where machine learning would be terrible, as it would not be 100% accurate and some passengers would not get picked up. A simple algorith works better here, and the question confirms customers will be indicating when they are at the stop so no ML required.
NEW QUESTION # 28
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Set the early stopping parameter to TRUE
- B. Change the search algorithm from Bayesian search to random search.
- C. Decrease the range of floating-point values
- D. Decrease the maximum number of trials during subsequent training phases.
- E. Decrease the number of parallel trials
Answer: B,C
NEW QUESTION # 29
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. Area Under the ROC Curve (AUC)
- D. Mean absolute percentage error (MAPE)
Answer: C
NEW QUESTION # 30
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. Split the training and test data based on time rather than a random split to avoid leakage
- B. Add more data to your test set to ensure that you have a fair distribution and sample for testing
- 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. Normalize the data for the training, and test datasets as two separate steps.
Answer: C
NEW QUESTION # 31
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. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
- B. Increase the dropout rate to 0.8 and retrain your model.
- C. Switch from CPU to GPU serving
- D. Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters
Answer: A
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 # 32
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. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- 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. Create a tf.data.Dataset.prefetch transformation
Answer: C
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 # 33
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?
- A. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
- B. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
- C. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
- D. Schedule a weekly query in BigQuery to compute the success metric.
Answer: D
Explanation:
Scheduling a weekly query in BigQuery to compute the success metric is a cost-effective way to monitor the model's performance. BigQuery allows you to run complex queries on large datasets in a cost-effective and performant manner. By using BigQuery, you can compute the success metric on a regular basis without incurring the additional costs of other services such as Vertex AI or Cloud Composer.
Additionally, by scheduling the query to run weekly, you can ensure that you are monitoring the model's performance in a timely manner, while still providing enough time for the model to degrade below the acceptable baseline. You can then use the results of the query to determine when retraining is necessary.
NEW QUESTION # 34
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
- A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
- B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
- C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
- D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
Answer: C
NEW QUESTION # 35
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?
- A. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
- B. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
- C. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
- D. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
Answer: A
Explanation:
Explanation
NEW QUESTION # 36
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- B. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- C. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- D. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
Answer: D
NEW QUESTION # 37
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
- A. Binary cross-entropy
- B. Sparse categorical cross-entropy
- C. Categorical cross-entropy
- D. Categorical hinge
Answer: B
Explanation:
se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]
NEW QUESTION # 38
Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?
- A. Remove the model and replace it with human moderation.
- B. Replace your model with a different text classifier.
- C. Add synthetic training data where those phrases are used in non-toxic ways
- D. Raise the threshold for comments to be considered toxic or harmful
Answer: C
Explanation:
This approach would help to improve the performance of the classifier by providing it with more examples of the religious phrases being used in non-toxic ways. This would allow the classifier to better differentiate between toxic and non-toxic comments that reference these religious groups. Additionally, synthetic data is a cost-effective way to improve the performance of an existing model without requiring a significant investment in human resources.
NEW QUESTION # 39
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?
- A. Manage your ML workflows with Vertex ML Metadata.
- B. Store your tf.logging data in BigQuery.
- C. Store all ML metadata in Google Cloud's operations suite.
- D. Manage all relational entities in the Hive Metastore.
Answer: C
NEW QUESTION # 40
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Which endpoints should the Enrichment Cloud Functions call?
- A. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
- B. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API
- C. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
- D. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
Answer: C
Explanation:
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
-The Cloud Function calls 3 different endpoints to enrich the ticket:
-An AI Platform endpoint, where the function can predict the priority.
-An AI Platform endpoint, where the function can predict the resolution time.
-The Natural Language API to do sentiment analysis and word salience.
-For each reply, the Cloud Function updates the Firebase real-time database.
-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.
NEW QUESTION # 41
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 # 42
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Google Professional-Machine-Learning-Engineer Daily Practice Exam New 2024 Updated 150 Questions: https://drive.google.com/open?id=1BnNy8f6PUMz2y3Wv9CRXlQj2Cy3vdTH2