What High Value Professional-Data-Engineer Real Exam Is

NEW QUESTION 1

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

• A. dataflow.worker
• B. dataflow.compute
• C. dataflow.developer
• D. dataflow.viewer

Answer: A

Explanation:
The dataflow.worker role provides the permissions necessary for a Compute Engine service account to execute work units for a Dataflow pipeline
Reference: https://cloud.google.com/dataflow/access-control

NEW QUESTION 2

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

• A. Load the data every 30 minutes into a new partitioned table in BigQuery.
• B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
• C. Store the data in Google Cloud Datastor
• D. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
• E. Store the data in a file in a regional Google Cloud Storage bucke
• F. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer: A

NEW QUESTION 3

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

• A. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP typ
• B. Reload the data.
• C. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numericvalues from the column TS for each ro
• D. Reference the column TS instead of the column DT from now on.
• E. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP value
• F. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
• G. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN typ
• H. Reload all data in append mod
• I. For each appended row, set the value of IS_NEW to tru
• J. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
• K. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP value
• L. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP typ
• M. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now o
• N. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Answer: D

NEW QUESTION 4

Your United States-based company has created an application for assessing and responding to user actions. The primary table’s data volume grows by 250,000 records per second. Many third parties use your application’s APIs to build the functionality into their own frontend applications. Your application’s APIs should comply with the following requirements:
Single global endpoint
ANSI SQL support
Consistent access to the most up-to-date data What should you do?

• A. Implement BigQuery with no region selected for storage or processing.
• B. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
• C. Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.
• D. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

Answer: B

NEW QUESTION 5

What are all of the BigQuery operations that Google charges for?

• A. Storage, queries, and streaming inserts
• B. Storage, queries, and loading data from a file
• C. Storage, queries, and exporting data
• D. Queries and streaming inserts

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION 6

You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You’ve collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

• A. Use Cloud Vision AutoML with the existing dataset.
• B. Use Cloud Vision AutoML, but reduce your dataset twice.
• C. Use Cloud Vision API by providing custom labels as recognition hints.
• D. Train your own image recognition model leveraging transfer learning techniques.

Answer: A

NEW QUESTION 7

Which methods can be used to reduce the number of rows processed by BigQuery?

• A. Splitting tables into multiple tables; putting data in partitions
• B. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause
• C. Putting data in partitions; using the LIMIT clause
• D. Splitting tables into multiple tables; using the LIMIT clause

Answer: A

Explanation:
If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day.
If you use the LIMIT clause, BigQuery will still process the entire table. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION 8

You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the
Internet so public initialization actions cannot fetch resources. What should you do?

• A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
• B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
• C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
• D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

Answer: D

NEW QUESTION 9

You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

• A. Batch job, PubSubIO, side-inputs
• B. Streaming job, PubSubIO, JdbcIO, side-outputs
• C. Streaming job, PubSubIO, BigQueryIO, side-inputs
• D. Streaming job, PubSubIO, BigQueryIO, side-outputs

Answer: A

NEW QUESTION 10

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model’, table user_features). How should you create the ML pipeline?

• A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• B. Create an Authorized View with the provided quer
• C. Share the dataset that contains the view with the application service account.
• D. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the quer
• E. Grant the Dataflow Worker role to the application service account.
• F. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.Write the results to Cloud Bigtable using BigtableI
• G. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION 11

The for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

• A. Cloud Dataflow connector
• B. DataFlow SDK
• C. BiqQuery API
• D. BigQuery Data Transfer Service

Answer: A

Explanation:
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
Reference: https://cloud.google.com/bigtable/docs/dataflow-hbase

NEW QUESTION 12

Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of datA. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

• A. Encrypted on Cloud Storage with user-supplied encryption key
• B. A separate decryption key will be given to each authorized user.
• C. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
• D. In Cloud SQL, with separate database user names to each use
• E. The Cloud SQL Admin activity logs will be used to provide the auditability.
• F. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.

Answer: B

NEW QUESTION 13

You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?

• A. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
• B. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
• C. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
• D. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

Answer: C

NEW QUESTION 14

When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

• A. Your gcloud does not have access to the BigQuery resources
• B. BigQuery cannot be accessed from local machines
• C. You are missing gcloud on your machine
• D. Pipelines cannot be run locally

Answer: A

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION 15

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

• A. Include multiple time series values within the row key
• B. Keep the row keep as an 8 bit integer
• C. Keep your row key reasonably short
• D. Keep your row key as long as the field permits

Answer: C

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION 16

You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

• A. Increase the share of the test sample in the train-test split.
• B. Try to collect more data and increase the size of your dataset.
• C. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
• D. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Answer: D

NEW QUESTION 17

Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

• A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
• B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
• C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
• D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Answer: C

NEW QUESTION 18

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

• A. Rewrite the job in Pig.
• B. Rewrite the job in Apache Spark.
• C. Increase the size of the Hadoop cluster.
• D. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Answer: A

NEW QUESTION 19

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

• A. Implement clustering in BigQuery on the ingest date column.
• B. Implement clustering in BigQuery on the package-tracking ID column.
• C. Tier older data onto Cloud Storage files, and leverage extended tables.
• D. Re-create the table using data partitioning on the package delivery date.

Answer: A

NEW QUESTION 20

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

• A. Linear regression
• B. Logistic classification
• C. Recurrent neural network
• D. Feedforward neural network

Answer: A

NEW QUESTION 21

An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

• A. Create and share an authorized view that provides the aggregate results.
• B. Create and share a new dataset and view that provides the aggregate results.
• C. Create and share a new dataset and table that contains the aggregate results.
• D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Answer: D

Explanation:
Reference: https://cloud.google.com/bigquery/docs/access-control

NEW QUESTION 22
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