IT業界での先駆者として、我々GoShikenの目的はIT認定試験に参加する皆様に助けを提供することです。我々のエリートたちは目標を達成するために、昼も夜も努力してMicrosoft試験の数年以来のデータの分析と整理に就職しています。彼らの真面目な態度があって、我々のDP-750対策を利用するお客様のほとんどはDP-750試験に合格できます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 2: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 4: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
GoShikenは専門的なIT認証サイトで、成功率が１００パーセントです。これは多くの受験生に証明されたことです。GoShikenにはIT専門家が組み立てられた団体があります。彼らは受験生の皆さんの重要な利益が保障できるように専門的な知識と豊富な経験を活かして特別に適用性が強いトレーニング資料を研究します。その資料が即ちMicrosoftのDP-750試験トレーニング資料で、問題集と解答に含まれていますから。
質問 # 70
You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main. The solution must preserve the commit history in the repository.
Which command should you run?
正解：B
解説：
To incorporate changes from your feature branch into the main branch while keeping every individual commit intact, you must use a fast-forward merge or a standard merge commit.
In the Azure Databricks Git folders UI or via standard Git operations, use the following command:
The Correct Command: git merge <branch-name>
Preserves History: Unlike a squash merge, a standard merge keeps all individual commit messages, authors, and timestamps.
Maintains Timeline: It seamlessly integrates the exact commit graph from your feature branch into the main branch.
Reference:
https://docs.databricks.com/aws/en/repos/git-operations-with-repos
質問 # 71
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df-fillna(0, subset=['order_amount'])
Does this meet the goal?
正解：A
解説：
The correct answer is B - No.
df.fillna(0, subset=['order_amount']) does not remove null rows. It replaces null values in order_amount with the integer 0, leaving those rows in the DataFrame with 0 as their order_amount value. The row count is unchanged - nulls are imputed, not dropped.
The requirement is to exclude rows where order_amount is null, meaning those rows should not appear in the result at all. fillna is a data imputation function - it fills gaps with default values, which is a different operation entirely from filtering. After fillna, a downstream process would see order_amount = 0 and might treat it as a valid zero-value order rather than recognising it was originally null.
The correct approach is df.dropna(subset=['order_amount']) or df.filter(df.order_amount.isNotNull()), both of which physically remove null rows from the resulting DataFrame.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
質問 # 72
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount != None)
Does this meet the goal?
正解：A
解説：
Correct:
* You run the following expression.
df.dropna(subset=["order_amount"])
The expression df.dropna(subset=["order_amount"]) is an appropriate and effective way to exclude rows where order_amount is null.
* You run the following expression.
df.filter(df.order_amount.isNotNull())
To exclude rows where the order amount is null, you can use the isNotNull() method or a SQL expression within the filter() or where() functions.Here are the standard, appropriate expressions:
Option 1: Python/PySpark API (Recommended)
pythondf_clean = df.filter(df["order_amount"].isNotNull())
Incorrect:
* You run the following expression.
df.fillna(0, subset=['order_amount'])
* You run the following expression.
df.filter(df.order_amount != None)
Reference:
https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/
https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna
質問 # 73
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?
正解：A
解説：
A Unity Catalog volume is the appropriate location because it provides governed file storage with permissions managed through Unity Catalog. After uploading the library package to the volume, it can be configured as a cluster library on Cluster1. This approach permits centralized access control, auditing, and lifecycle management. Option A installs the library without placing its source under Unity Catalog governance. Option B provides notebook-scoped dependency management but does not, by itself, satisfy the requirement that Unity Catalog control access to the library artifact. A schema is a logical container for tables, views, functions, models, and volumes; a library file cannot be uploaded directly to the schema itself, eliminating option D. Unity Catalog volumes explicitly support storing cluster libraries and job dependencies. Microsoft Learn
質問 # 74
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook 1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
The solution is a Key Vault-backed secret scope combined with dbutils.secrets.get() in the notebook.
An Azure Key Vault-backed secret scope does not copy the secret value into Databricks. Every call to dbutils.
secrets.get() fetches the live current value directly from Key Vault at runtime. This means when the cloud security team rotates storageKey in Key Vault, the notebook automatically picks up the new value on its next run - no Databricks configuration changes needed. That satisfies 'always retrieve the latest value.' The principle of least privilege is upheld because only the secret scope has read access to Key Vault through a service principal or managed identity, and individual users never see the raw secret value - Databricks redacts it in logs automatically.
A Databricks-backed scope stores a copy of the secret inside Databricks, so it wouldn't automatically reflect Key Vault updates. Hardcoding or using environment variables would expose the secret in plain text.
Reference: https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secret-scopes
質問 # 75
......
質の良いMicrosoftのDP-750試験トレーニング資料が見つけられないので、まだ悩んでいますか。GoShikenのMicrosoftのDP-750試験トレーニング資料は豊富な経験を持っているIT専門家が研究したもので、問題と解答が緊密に結んでいるものです。他のサイトの資料はそれと比べることすらできません。その正確性も言うまでもありません。GoShikenを選ぶのは成功を選ぶのに等しいと言えます。
DP-750赤本合格率: https://www.goshiken.com/Microsoft/DP-750-mondaishu.html