[Apr-2024] The Google Professional-Machine-Learning-Engineer Exam Test For Brief Preparation
Revolutionary Guide To Exam Google Dumps
Google Professional Machine Learning Engineer Exam is a comprehensive program that covers a wide range of topics related to machine learning. Professional-Machine-Learning-Engineer exam consists of multiple-choice questions, coding challenges, and hands-on tasks that evaluate the candidate's practical skills and knowledge. By earning this certification, candidates can demonstrate their proficiency in machine learning and stand out in a competitive job market.
NEW QUESTION # 121
You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?
- A. Update the model monitoring job to use the more recent training data that was used to retrain the model.
- B. Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.
- C. Update the model monitoring job to use a lower sampling rate.
- D. Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint
Answer: D
NEW QUESTION # 122
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the dat a. You want to make your model training pipeline more robust to issues like this. What should you do?
- A. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.
- B. Use TensorFlow Data Validation to detect and flag schema anomalies.
- C. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don't match the schema with 0.
- D. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
Answer: C
NEW QUESTION # 123
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?
- A. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
- B. Rewrite your input function to resize and reshape the input images.
- C. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
- D. Rewrite your input function using parallel reads, parallel processing, and prefetch.
Answer: D
Explanation:
The trace in the question shows that the training time is taking longer than expected. This is likely due to the input function not being optimized. To decrease training time in a cost-efficient way, the best option is to rewrite the input function using parallel reads, parallel processing, and prefetch. This will allow the model to process the data more efficiently and decrease training time. References:
* [Cloud TPU Performance Guide]
* [Data input pipeline performance guide]
NEW QUESTION # 124
You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?
- A. Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.
- B. Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.
- C. Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.
- D. Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.
Answer: A
NEW QUESTION # 125
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?
- A. Train an anomaly detection model instead of an MLP
- B. Train an XGBoost model instead of an MLP
- C. Gather more data using Amazon Mechanical Turk and then retrain
- D. Add class weights to the MLP's loss function and then retrain
Answer: B
NEW QUESTION # 126
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?
- A. Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.
- B. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
- C. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
- D. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
Answer: D
NEW QUESTION # 127
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
- A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real- time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- C. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
- D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
Answer: B
Explanation:
Explanation
NEW QUESTION # 128
You recently trained an XGBoost model on tabular data You plan to expose the model for internal use as an HTTP microservice After deployment you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?
- A. Deploy the model to BigQuery ML by using CREATE model with the BOOSTED-THREE-REGRESSOR statement and invoke the BigQuery API from the microservice.
- B. Build a Flask-based app Package the app in a custom container on Vertex Al and deploy it to Vertex Al Endpoints.
- C. Use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex Al Endpoints.
- D. Build a Flask-based app Package the app in a Docker image and deploy it to Google Kubernetes Engine in Autopilot mode.
Answer: C
NEW QUESTION # 129
You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?
- A. Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.
- B. Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.
- C. Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.
- D. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.
Answer: A
Explanation:
Entity extraction is a natural language processing (NLP) task that involves identifying and extracting specific types of information from text, such as names, dates, locations, etc. Entity extraction can help you analyze a corpus of recipes and extract each ingredient and cookware mentioned in them. Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. Itprovides a service for AutoML entity extraction, which allows you to create and train custom entity extraction models without writing any code. You can use Vertex AI to create a text dataset for entity extraction, and label your data with two entities:
"ingredient" and "cookware". You need to label at least 200 examples of each entity type to train an AutoML entity extraction model. You can also use a holdout dataset to evaluate the performance of your model, such as precision, recall, and F1-score. This solution can help you build a machine learning model to scan a corpus of recipes and extract each ingredient and cookware mentioned in them, and use the results to help users with meal planning. References:
* AutoML Entity Extraction | Vertex AI
* Preparing data for AutoML Entity Extraction | Vertex AI
NEW QUESTION # 130
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?
- A. Create one-hot word encoding vectors.
- B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
- C. Download word embeddings pre-trained on a large corpus.
- D. Create word embedding vectors that store edit distance with every other word.
Answer: A
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/
NEW QUESTION # 131
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?
- A. Predict the missing values using linear regression.
- B. Apply feature crossing with another column that does not have missing values.
- C. Delete the rows that have missing values.
- D. Replace the missing values with zeros.
Answer: A
NEW QUESTION # 132
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?
- A. Use an iterative dropout technique to identify which features do not degrade the model when removed.
- B. Use Principal Component Analysis to eliminate the least informative features.
- C. Use L1 regularization to reduce the coefficients of uninformative features to 0.
- D. After building your model, use Shapley values to determine which features are the most informative.
Answer: C
Explanation:
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview#sampled-shapley
NEW QUESTION # 133
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 using a less complex algorithm.
- B. Address data leakage by applying nested cross-validation during model training.
- C. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- D. Address data leakage by removing features highly correlated with the target value.
Answer: B
NEW QUESTION # 134
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 model performance is monitored
- C. Ensure that feature expectations are captured in the schema
- D. Ensure that training is reproducible
Answer: D
NEW QUESTION # 135
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?
- A. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 100.
- B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100
- C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 1.
- D. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1
Answer: C
NEW QUESTION # 136
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 the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- 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 Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
Answer: D
NEW QUESTION # 137
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
- A. 501*256+257*128+2 = 161154
- B. 500*256+256*128+128*2 = 161024
- C. 501*256+257*128+128*2=161408
- D. 500*256*0 25+256*128*0 25+128*2 = 40448
Answer: D
NEW QUESTION # 138
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. Decrease the number of parallel trials
- C. Change the search algorithm from Bayesian search to random search.
- D. Decrease the range of floating-point values
- E. Decrease the maximum number of trials during subsequent training phases.
Answer: A,E
Explanation:
Reference:
https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning#early-stopping
NEW QUESTION # 139
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website.
Which result should you use to determine whether the model is successful?
- A. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
- B. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
- C. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.
- D. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
Answer: D
NEW QUESTION # 140
A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?
- A. Compressed JSON
- B. RecordIO
- C. Parquet files
- D. CSV files
Answer: C
NEW QUESTION # 141
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