[Aug-2023] Dumps Brief Outline Of The Professional-Machine-Learning-Engineer Exam - SurePassExams
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Google Professional Machine Learning Engineer exam requires hands-on experience with the following while also including advanced knowledge of machine learning and expertise in designing and implementing appropriate ML architectures: Data preprocessing, Feature engineering, Model building, Model deployment, Model monitoring, Outlier detection, Hyperparameter tuning, and Algorithm selection. The Google Professional Machine Learning Engineer certification aims to authenticate these expertise areas along with practical experience to validate oneself as a versatile, employable programming professional.
Google Professional-Machine-Learning-Engineer Certification Exam is a professional-level certification exam that tests your proficiency in building and deploying machine learning models on Google Cloud Platform. It is designed for individuals with a solid understanding of machine learning concepts and experience in developing and deploying machine learning models on Google Cloud Platform. If you are a machine learning engineer, data scientist, or software developer looking to demonstrate your expertise in machine learning, this certification exam is an excellent way to showcase your skills and knowledge.
NEW QUESTION # 49
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 "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- B. Import your user events and then your product catalog to make sure you have the highest quality event stream
- C. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- 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: A
Explanation:
Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.
NEW QUESTION # 50
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. Keep the original test dataset unchanged even if newer products are incorporated into retraining
- C. Replace your test dataset with images of the newer products when they are introduced to retraining.
- D. Extend your test dataset with images of the newer products when they are introduced to retraining
Answer: C
NEW QUESTION # 51
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
- A. Z-normalize all the numeric features.
- B. Use one-hot encoding on all categorical features.
- C. Oversample the fraudulent transaction 10 times.
- D. Write your data in TFRecords.
Answer: C
NEW QUESTION # 52
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
- A. Create a cluster on Dataproc for training
- B. Create a Managed Instance Group with autoscaling
- C. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
- D. Use Al Platform for distributed training
Answer: D
Explanation:
Explanation:
NEW QUESTION # 53
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 "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- B. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- 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: C
NEW QUESTION # 54
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. 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.
- C. There is not a sufficient amount of training data.
- 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 # 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. Change the learning rate
- B. Reduce the batch size
- C. Change the optimizer
- D. Reduce the image shape
Answer: C
NEW QUESTION # 56
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?
- A. Create an appropriate pronunciation lexicon.
- B. Output speech marks to guide in pronunciation.
- C. Use Amazon Lex to preprocess the text files for pronunciation
- D. Convert current documents to SSML with pronunciation tags.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/polly/latest/dg/ssml.html
NEW QUESTION # 57
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
- A. Cloud Composer, Vertex AI Training with custom containers, and App Engine
- B. Vertex AI Pipelines and App Engine
- C. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
- D. Cloud Composer, BigQuery ML, and Vertex AI Prediction
Answer: B
NEW QUESTION # 58
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)
- A. Cross-validation
- B. Logarithmic transformation
- C. Numerical value binning
- D. High-degree polynomial transformation
- E. One hot encoding
Answer: A,C
NEW QUESTION # 59
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. Normalize the data for the training, and test datasets as two separate steps.
- D. Add more data to your test set to ensure that you have a fair distribution and sample for testing
Answer: A
NEW QUESTION # 60
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory dat a. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?
- A. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
- B. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
- C. Use the TFX ModelValidator tools to specify performance metrics for production readiness
- D. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
Answer: C
Explanation:
https://www.tensorflow.org/tfx/guide/evaluator
NEW QUESTION # 61
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines
* Provide a quick and easy way to understand metadata
Which approach meets these requirements?
- A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
- B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
- C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
- D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
Answer: B
NEW QUESTION # 62
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?
- A. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
- B. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
- C. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
- D. Execute a query in BigQuery to retrieve all the existing table names in your project using the
Answer: C
Explanation:
INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
Explanation:
A should be the way to go for large datasets --This is also good but it is legacy way of checking:- NFORMATION_SCHEMA contains these views for table metadata: TABLES and TABLE_OPTIONS for metadata about tables. COLUMNS and COLUMN_FIELD_PATHS for metadata about columns and fields. PARTITIONS for metadata about table partitions (Preview)
NEW QUESTION # 63
You work on a growing team of more than 50 data scientists who all use AI 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. 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.
- 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. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
- D. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
Answer: C
NEW QUESTION # 64
You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?
- A. Randomly select a 10 gigabyte subset of the data to train your model.
- B. Set the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.
- C. Preprocess the input CSV file into a TFRecord file.
- D. Split into multiple CSV files and use a parallel interleave transformation.
Answer: B
NEW QUESTION # 65
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
- A. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
- B. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
- C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
- D. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
Answer: B
NEW QUESTION # 66
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
- A. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
- B. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
- C. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
- D. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
Answer: B
Explanation:
Explanation
NEW QUESTION # 67
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- C. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- D. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
Answer: A
NEW QUESTION # 68
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?
- A. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.
- B. Use AI Platform to run distributed training jobs without checkpoints.
- C. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
- D. Use AI Platform to run distributed training jobs with checkpoints.
Answer: A
NEW QUESTION # 69
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