[Oct-2023] Updated Google Cloud Certified Professional-Machine-Learning-Engineer Exam Questions BUNDLE PACK
Master The Google Content Professional-Machine-Learning-Engineer EXAM DUMPS WITH GUARANTEED SUCCESS!
NEW QUESTION # 53
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?
- A. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
- B. Deploy and version the model on Al Platform.
- C. Export the model to BigQuery ML.
- D. Use Dataflow with the SavedModel to read the data from BigQuery
Answer: C
NEW QUESTION # 54
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 Batch job, and an external Apache Hive metastore to search and discover metadata.
- B. 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.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION # 55
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
- A. Build a collaborative-based filtering model
- B. Build a knowledge-based filtering model
- C. Build a regression model using the features as predictors
- D. Build a classification model
Answer: A
NEW QUESTION # 56
You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?
- A. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
- B. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
- C. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
- D. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL
Answer: D
NEW QUESTION # 57
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
* Combine multiple data sources.
* Reuse existing PySpark logic.
* Run the solution on the existing schedule.
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
- A. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a
"processed" location in Amazon S3 that is accessible for downstream use. - B. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a
"processed" location in Amazon S3 that is accessible for downstream use. - C. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- D. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
Answer: B
Explanation:
Explanation
NEW QUESTION # 58
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
- A. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
- B. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
- C. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
- D. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
Answer: A
Explanation:
Google has bought this software and support for this tool is not good. SQL can work in Cloud fusion pipelines too but I would prefer to use a single tool like Bigquery to both transform and store data.
NEW QUESTION # 59
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. 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.
- 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 training-serving skew in your production environment.
- D. There is not a sufficient amount of training data.
Answer: C
NEW QUESTION # 60
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
- A. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
- B. Three individual features binned latitude, binned longitude, and one-hot encoded car type
- C. One feature obtained as an element-wise product between latitude, longitude, and car type
- D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
Answer: A
NEW QUESTION # 61
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?
- A. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
- B. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
- C. Load the data into Cloud Bigtable, and read the data from Bigtable
- D. Load the data into BigQuery and read the data from BigQuery.
Answer: A
NEW QUESTION # 62
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: A
Explanation:
Refer to this link for details: https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas First 2 points talks about querying the data.
Download query results to a pandas DataFrame by using the BigQuery Storage API from the IPython magics for BigQuery in a Jupyter notebook.
Download query results to a pandas DataFrame by using the BigQuery client library for Python.
Download BigQuery table data to a pandas DataFrame by using the BigQuery client library for Python.
Download BigQuery table data to a pandas DataFrame by using the BigQuery Storage API client library for Python.
https://googleapis.dev/python/bigquery/latest/magics.html#ipython-magics-for-bigquery
https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas
NEW QUESTION # 63
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. Change the search algorithm from Bayesian search to random search.
- B. Set the early stopping parameter to TRUE
- C. Decrease the range of floating-point values
- D. Decrease the number of parallel trials
- E. Decrease the maximum number of trials during subsequent training phases.
Answer: A,C
NEW QUESTION # 64
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?
- A. Area under the curve
- B. Root Mean Square Error (RMSE)
- C. Residual plots
- D. Confusion matrix
Answer: A
NEW QUESTION # 65
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?
- A. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.
- B. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
- C. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket
- D. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
- E. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket
Answer: B
NEW QUESTION # 66
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. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- B. Create a new view with BigQuery that does not include a column with city information
- C. 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.
- D. 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.
Answer: D
NEW QUESTION # 67
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. Manage all relational entities in the Hive Metastore.
- D. Store all ML metadata in Google Cloud's operations suite.
Answer: D
NEW QUESTION # 68
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. Create a tf.data.Dataset.prefetch transformation
- B. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- C. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- D. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
Answer: B
NEW QUESTION # 69
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?
- A. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
- B. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
- C. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
- D. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
Answer: B
Explanation:
Explanation
NEW QUESTION # 70
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?
- A. Amazon Kinesis Data Streams
- B. Amazon Kinesis Data Analytics
- C. Amazon Kinesis Data Firehose
- D. AWS DMS
Answer: C
NEW QUESTION # 71
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 Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
- C. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
- D. 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
Answer: C
Explanation:
https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.
https://www.kubeflow.org/docs/components/pipelines/introduction/#what-is-kubeflow-pipelines
"Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI." https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
NEW QUESTION # 72
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Earning the Google Professional Machine Learning Engineer certification demonstrates to employers and clients that an individual has the skills and knowledge to design, build, and deploy machine learning models using Google Cloud technologies. It also opens up opportunities for career growth and advancement in the field of machine learning.
Google Professional Machine Learning Engineer Certification Exam is an industry-leading certification that validates the skills and knowledge required to design, build, and deploy scalable and reliable machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification is designed for professionals who have experience in developing and deploying machine learning models in a production environment and are looking to advance their career in machine learning.
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